802.11 localization algorithms provide the ability to accurately position and track wireless clients thereby enabling location-based services and applications . However , we show that these localization techniques are vulnerable to noncryptographic attacks where an adversary uses a low-cost directional antenna to appear from the localization algorithm 's perspective to be in another arbitrary location of their choosing . The attacker 's ability to actively influence where they are positioned is a key distinguishing feature of the directional attack relative to prior localization attacks that use transmit power control to introduce localization errors . Node localizations techniques are a critical part in many wireless sensor network ( WSN ) applications . There are several challenges for building an efficient localization system that is suitable for wireless sensor networks , mainly , reducing computational complexity and communication overhead . For example , solving an over constraint set of linear equations via least square approximations and utilizing square root operations are computationally intensive . One of the severe conditions that needs to be taken into consideration is localizing a mobile target through a dispersed sensor network in the presence of physical barrier attacks . These attacks confuse the localization process and cause location estimation errors . Range-based methods , like the received signal strength indication ( RSSI ) , face the major influence of this kind of attack . We propose an automatic image inspection system to inspect for faults in coupler yokes during the running of a freight train . The inspection process is divided into two parts : the localization part and the recognition part . In the localization part , we propose multiple dimension features , design a fast algorithm to compute multiresolution image features , and use a linear support vector machine classifier to locate the position of the coupler yoke . Index space transformations are used to regularize the DWT algorithm and to avoid data collisions due to multiprojection . A summary of the data dependence and localization analysis is presented , as well as an array of L Processing Elements ( PEs ) for computing any J-octave DWT decomposition with latency of M , where L is the wavelet filter length and M is the input sequence length . The latency is independent of the highest computable octave J , for any value of J , and the efficiency is nearly optimal and independent of M. The proposed design is the fastest parallel implementation of the 1-D DWT with L PEs that we know of . We utilized a 3-axis accelerometer , 3-axis gyroscope , 3-axis magnetometer combined with the random forest machine learning algorithm . With an accuracy of 99.63 % for the right-handed model and an accuracy of 99.71 % for the left-handed model , an evaluation of the localization detection approach showed an average accuracy of 9.0 % . This shows that the brushing habits might be too variable to be captured by the random forest algorithm or that the data are not sufficient to generalize the pattern . Recent advances in 3D sensing technologies , as well as in inertial measurement technologies , have resulted in significant improvements in the accuracy of the localization of systems that combine all these sensors . Project Tango is one of the most successful examples of such systems . Since the present algorithms are on the assumption of correct beacon information , when the network is attacked , especially the beacons are captured by opponent , all algorithms would not work . To address this issue , one protocol which does not increase the number of beacon or even do not use the beacon on the basis of the thought of validation node SLPDVNM which can realize localization by dynamic validation node combining some kind of localization algorithm mechanism was presented . Secure analysis and simulation of protocol performance based on MMSE localization algorithm show that the proposed protocol not only can overcome the infection of vicious beacon but also has good resistance ability against some known attack . In this paper , the systematic investigation of a VLC channel is performed for both direct and indirect line of sight ( LoS ) by utilizing the impulse response of indoor optical wireless channels . In order to examine the localization scenario , two light-emitting diode ( LED ) grid patterns are used . The received signal strength ( RSS ) is observed based on the positional dilution of precision ( PDoP ) , a subset of the dilution of precision ( DoP ) used in global navigation satellite system ( GNSS ) positioning . The error percentage value based on the selection of the best SNR method is 0.95 % . The error percentage value based on localization technique method is 0.78 % . Design of WOW framework is motivated by the cognitive model of human visual perception and feature integration theory ( FIT ) . The key contributions in the proposed approach are : ( i ) Feedback mechanism between Recognition ( " What " ) and Localization ( " Where " ) modules ( both supervised ) , for a cohesive decision based on mutual consensus ; ( ii ) Hierarchy of visual features ( based on FIT ) for an efficient recognition task . Integration of information from the two channels ( " What " and " Where " ) in an iterative feedback mechanism , helps to filter erroneous contents in the outputs of individual modules . The detection task differs from localization in that there can be any number of objects in each image ( including zero ) , and false positives are penalized by the mean average precision ( mAP ) measure . The localization task is a convenient intermediate step between classification and detection , and allows us to evaluate our localization method independently of challenges specific to detection ( such as learning a background class ) . In Fig . Fine - Grained Visual Classification ( FGVC ) datasets contain small sample sizes , along with significant intra - class variation and interclass similarity . While prior work has addressed intra - class variation using localization and segmentation techniques , inter - class similarity may also affect feature learning and reduce classification performance . In this work , we address this problem using a novel optimization procedure for the end - to - end neural network training on FGVC tasks . On Holidays dataset , the Neural codes win the extreme short code category , while off - the - shelf NetVlad performs the best on 256D and higher . We additionally combine MAC and R - MAC with recent localization method for re - ranking to further boost the performance . Our scores compete with state - of - the - art systems based on local features and query expansion . The loss per example RoI is the sum of a classification log loss that encourages predicting the correct object ( or background ) label and a localization loss that encourages predicting an accurate bounding box ( see for details ) . To share conv network computation between RoIs , SGD mini - batches are created hierarchically . OverFeat . Sermanet et al . train a convolutional neural network to perform localization and adapt that localizer to perform detection . OverFeat efficiently performs sliding window detection but it is still a disjoint system . We introduce Complex Shape Histogram , a core component of our artificial intelligence engine , used for classifying 3D point clouds with a Support Vector Machine . We use Complex Shape Histograms also for loop closing detection in the simultaneous localization and mapping algorithm . Our intelligent mobile system is built on top of the Qualitative Spatio-Temporal Representation and Reasoning framework . In practice , this can require the re-computation of the map when the estimated robot poses change , as when a loop closure is detected . This paper presents a parallelization of a reference implementation of the grid mapping algorithm , which is suitable to be fully run on a graphics card showing huge processing speedups ( up to 50X ) while fully releasing the main processor , which can be very useful for many Simultaneous Localization and Mapping algorithms . The first signal processor ( 200 ) is electrically connected to the three-dimensional point cloud scanning instrument ( 120 ) and the sensing component ( 110 ) respectively . The first signal processor ( 200 ) is used to update location and status information of a scanning center of the movable scanning device ( 100 ) in real time by means of the simultaneous positioning and mapping algorithm on the basis of data collected by the sensing component ( 110 ) , and is further used to register point cloud data corresponding to any scanning center to a world coordinate system on the basis of real-time location and status information of the scanning center . Aiming at the high hardware cost of simultaneous positioning and mapping ( SLAM ) for mobile robots , a mobile robot system using LIDAR(Light Detection and Ranging ) for obtaining date observing is designed . The experiments Based on robotic operating system ( ROS ) , using laser radar to acquire 2D laser scanned matching data and an open source GMapping software package for SLAM , RVIZ ( 3D visualization tool for ROS ) is adopted to achieve indoor mapping in an unknown environment . However , instabilities in sensor position data acquisition remain , because the indoor environment is more complex than the outdoor environment . On the other hand , simultaneous localization and mapping processing is better than indoor positioning for measurement accuracy and sensor cost . However , it is not easy to estimate position data from a single viewpoint directly . In addition , two levels of sensor data are provided for the purpose of assisting in the complete validation of algorithms using consumer-grade sensors . A forward-facing stereo camera was utilized to capture visual images of the environment and the position information of the vehicle that was estimated through simultaneous localization mapping ( SLAM ) are offered as a baseline . This paper presents 3D map data generated by the SLAM algorithm in the LASer ( LAS ) format for a wide array of research purposes , and a file player and a data viewer have been made available via the Github webpage to allow researchers to conveniently utilize the data in a Robot Operating System ( ROS ) environment . We present a method to make an accurate and dense reconstruction from the input of video captured by a free moving handheld camera in real time . By the method firstly , the positions of the camera and sparse 3D points are estimated by simultaneous localization mapping . Then the depth maps of selected reference frames are computed from corresponding camera bundles . In this paper , a normal distribution transform (NDT)-based approach for grid map registration is presented . For simultaneous mapping and localization approaches on laser data , the NDT is widely used to align new laser scans to reference scans . The original grid quantization-based NDT results in good registration performances but has poor convergence properties due to discontinuities of the optimization function and absolute grid resolution . In recent years , application fields such as industrial automation and indoor robot navigation increased the demand on reliable localization systems . Simultaneous mapping and localization systems often depend on depth imaging in order to reconstruct the scene . Time-of-Flight sensors prove to be well suited for these applications , however are impaired by different error sources . At the very beginning , the digital image preprocessing methods are used to obtain the static feature point in radar images . Subsequently , the trajectory of the vessel is calculated based on a simultaneous localization and mapping – based algorithm . Finally , the calculated vessel trajectory is compared with the actual trajectory to verify the validity of the proposed approach . The loss of observability resulting from the use of a single monocular camera is dealt with by constraining the target vehicle to follow ground terrain . An unscented Kalman filter ( UKF ) provides a simultaneous localization and mapping solution for the estimation of aircraft states and feature locations , which define the target 's local environment . Another filter , a loosely coupled Kalman filter for the target states , receives 3D measurements of target position with estimated covariance obtained by an unscented transformation ( UT ) . 3D reconstruction has been well studied based on the multi - view geometry ( MVG ) in the literature . The major research directions include structure from motion ( SfM ) for large - scale high - quality reconstruction and simultaneous localization and mapping ( SLAM ) for navigation . Though they are very successful in these scenarios , they are restricted by 1 ) the coverage that the multiple views can give and 2 ) the appearance of the object that wants to reconstruct . The system is composed of three central components : tracking , mapping and loop closing which are based on ORB-SLAM2 [ 1 ] . The tracking part estimates the pose of a frame via optimizing both the reprojection error and inverse depth error of matching feature points , rather than just optimizing the reprojection error like ORB-SLAM2 [ 1 ] . The mapping part optimizes the pose of all keyframes and the position of all map points by using bundle adjustment , and an occupancy grid map of the surrounding environment is constructed based on the octree-based mapping framework OctoMap [ 2 ] for high-level tasks , and the map is more useful then the sparse points representation in ORB-SLAM2 [ 1 ] . To make this system more robust and more rapid , We suggest an improved method to estimate camera-posing , where we use a subset of extracted points for estimation , avoiding mixing large-reprojection-error points in bundle adjustment . The experimental results on the SLAM benchmark dataset KITTI odometry demonstrated that the proposed method outperformed the original implementation of ORB-SLAM2 from the viewpoint of calculation time . We use multiple triplets of planar segments to generate candidate probability distribution and employ it to find the most probable pose with respect to a global map of planar segments . The PlaneLoc implementation uses the ORB-SLAM2 system that serves as visual odometry and makes it possible to generate observation in a form of sets of local segments online . The proposed approach can be used for global localization with a known map or for loop closing and re-localization in Simultaneous Localization and Mapping . A dataset of simulated aerial imagery is used for experiments . The experiment results are shown , that the particle filter localization algorithm using the logistic function was able to surpass the accuracy of state-of-the-art ORB-SLAM2 algorithm by 2.6 times . The algorithm is shown to be able to navigate using up-to-date maps more accurately and with an average decrease in precision by 30 % using out-of-date maps . The robot vision shows excellent measurement performance for obtaining depth information and recognizing unknown object . Additionally , our system is capable of SLAM when tested in a complicated environment by using the well-known ORB-SLAM2 algorithm . The experimental results shows that the average tracking time is ~0.031s , which is below the inverse of the camera frame rate ( 0.033s ) , thus demonstrating a real-time performance . The state of art Visual SLAM is going from sparse feature to semi-dense feature to provide more information for environment perception , whereas the semi-dense methods often suffer from inaccurate depth map estimation and are easy to become instable for some real-world scenarios . The paper proposes to extend the ORB-SLAM2 framework , which is a robust sparse feature SLAM system tracking camera motion with map maintenance and loop closure , by introducing the unified spherical camera model and the semi-dense depth map . The unified spherical camera model fits the omnidirectional camera well , therefore the proposed Visual SLAM system could handle fisheye cameras which are commonly installed on modern vehicles to provide larger perceiving region . In this work , we take state-of-the-art patch comparison CNNs , train them from scratch and analyze the performance on the KITTI odometry benchmark . For that , we replace the ORBfrontend within the publicly available ORB-SLAM2 framework through our trained CNN variants and compare both . We show that it is necessary to downsize the complexity of the original architectures to achieve real-time capability . Porting algorithms originally designed for desktop CPUs on those boards is not straightforward due to hardware limitations . In this paper , we present how we modified and customized the open source SLAM algorithm ORB-SLAM2 to run in real-time on the NVIDIA Jetson TX2 . We adopted a data flow paradigm to process the images , obtaining an efficient CPU/GPU load distribution that results in a processing speed of about 30 frames per second . Quantitative experimental results on four different sequences of the KITTI datasets demonstrate the effectiveness of the proposed approach . The source code of our data flow ORB-SLAM2 algorithm is publicly available on GitHub . Then , we propose and implement , on top of the Linux operating system , an environment for efficient execution of applications modeled as KPNs . Finally , we perform a simple design space exploration ( DSE ) to investigate the trade-off between system performance and power consumption when alternative ORB-SLAM2 KPNs are executed on different configurations of the ODROID-XU4 platform . The obtained results of this DSE clearly show the feasibility of running ORB-SLAM2 on ODROID-XU4 in real time with a limited power budget for a given range of flying time , thereby enabling cognitive autonomy on small drones . In order to construct the 3D point cloud map , the color information in the image needs to be transformed and combined with the depth information captured by the stereo camera . In this paper , we construct a binocular SLAM framework called MYNT-vSLAM to improve the sparse mapping of ORB-SLAM2 . Experimental results verified the feasibility of this algorithm , which is robust and fast in complex indoor environment , and can be applied to location and 3D point cloud map construction . The experimental results show that the proposed method is superior the state-of-the-art methods on both raw LiDAR scans dataset and semi-dense annotation dataset . Furthermore , the errors of the sparse depth produced by stereo ORB-SLAM2 are evaluated and this sparse depth and a single image are fed into the proposed model to further demonstrate the superiority of the proposed work . Besides , even though there are no moving objects in the frame , there is space for further improvement in accuracy . This article improves the feature point selection based on monocular ORB-SLAM2 system , by creatively using the idea comes from optical flow and then using the K-Means algorithm to classify the matched feature point pairs . The existing open source datasets are used for evaluating the improvement . Classification component is trained online to guarantee high discriminative power in the presence of distractors . Target estimation is performed by the IoU-predictor network inspired by the IoU-Net which was recently proposed for object detection as an alternative to typical anchor-based bounding box regression techniques . In this work , we further enhance the performance of ATOM by embedding Squeeze-and-Excitation ( SE ) blocks into IoU-Net in ATOM to recalibrate useful features and suppress useless features and obtain ATOMFR . To solve the abnormal changes in the target box in ATOMFR , we add the Relocation Module on ATOMFR and get ATOMFR ( RL ) . The predicted IoU is used as the detection confidence for NMS . In order to obtain a more accurate IoU prediction , we propose a 3D IoU-Net with IoU sensitive feature learning and an IoU alignment operation . To obtain a perspective-invariant prediction head , we propose an Attentive Corner Aggregation ( ACA ) module by aggregating a local point cloud feature from each perspective of eight corners and adaptively weighting the contribution of each perspective with different attentions . This makes properly localized bounding boxes degenerate during iterative regression or even suppressed during NMS . In the paper we propose IoU-Net learning to predict the IoU between each detected bounding box and the matched ground-truth . The network acquires this confidence of localization , which improves the NMS procedure by preserving accurately localized bounding boxes .
802.11 localization algorithms provide the ability to accurately position and track wireless clients thereby enabling location-based services and applications . However , we show that these localization techniques are vulnerable to noncryptographic attacks where an adversary uses a low-cost directional antenna to appear from the localization algorithm 's perspective to be in another arbitrary location of their choosing . The attacker 's ability to actively influence where they are positioned is a key distinguishing feature of the directional attack relative to prior localization attacks that use transmit power control to introduce localization errors .
Node localizations techniques are a critical part in many wireless sensor network ( WSN ) applications . There are several challenges for building an efficient localization system that is suitable for wireless sensor networks , mainly , reducing computational complexity and communication overhead . For example , solving an over constraint set of linear equations via least square approximations and utilizing square root operations are computationally intensive .
One of the severe conditions that needs to be taken into consideration is localizing a mobile target through a dispersed sensor network in the presence of physical barrier attacks . These attacks confuse the localization process and cause location estimation errors . Range-based methods , like the received signal strength indication ( RSSI ) , face the major influence of this kind of attack .
We propose an automatic image inspection system to inspect for faults in coupler yokes during the running of a freight train . The inspection process is divided into two parts : the localization part and the recognition part . In the localization part , we propose multiple dimension features , design a fast algorithm to compute multiresolution image features , and use a linear support vector machine classifier to locate the position of the coupler yoke .
Index space transformations are used to regularize the DWT algorithm and to avoid data collisions due to multiprojection . A summary of the data dependence and localization analysis is presented , as well as an array of L Processing Elements ( PEs ) for computing any J-octave DWT decomposition with latency of M , where L is the wavelet filter length and M is the input sequence length . The latency is independent of the highest computable octave J , for any value of J , and the efficiency is nearly optimal and independent of M. The proposed design is the fastest parallel implementation of the 1-D DWT with L PEs that we know of .
We utilized a 3-axis accelerometer , 3-axis gyroscope , 3-axis magnetometer combined with the random forest machine learning algorithm . With an accuracy of 99.63 % for the right-handed model and an accuracy of 99.71 % for the left-handed model , an evaluation of the localization detection approach showed an average accuracy of 9.0 % . This shows that the brushing habits might be too variable to be captured by the random forest algorithm or that the data are not sufficient to generalize the pattern .
Recent advances in 3D sensing technologies , as well as in inertial measurement technologies , have resulted in significant improvements in the accuracy of the localization of systems that combine all these sensors . Project Tango is one of the most successful examples of such systems .
Since the present algorithms are on the assumption of correct beacon information , when the network is attacked , especially the beacons are captured by opponent , all algorithms would not work . To address this issue , one protocol which does not increase the number of beacon or even do not use the beacon on the basis of the thought of validation node SLPDVNM which can realize localization by dynamic validation node combining some kind of localization algorithm mechanism was presented . Secure analysis and simulation of protocol performance based on MMSE localization algorithm show that the proposed protocol not only can overcome the infection of vicious beacon but also has good resistance ability against some known attack .
In this paper , the systematic investigation of a VLC channel is performed for both direct and indirect line of sight ( LoS ) by utilizing the impulse response of indoor optical wireless channels . In order to examine the localization scenario , two light-emitting diode ( LED ) grid patterns are used . The received signal strength ( RSS ) is observed based on the positional dilution of precision ( PDoP ) , a subset of the dilution of precision ( DoP ) used in global navigation satellite system ( GNSS ) positioning .
The error percentage value based on the selection of the best SNR method is 0.95 % . The error percentage value based on localization technique method is 0.78 % .
Design of WOW framework is motivated by the cognitive model of human visual perception and feature integration theory ( FIT ) . The key contributions in the proposed approach are : ( i ) Feedback mechanism between Recognition ( " What " ) and Localization ( " Where " ) modules ( both supervised ) , for a cohesive decision based on mutual consensus ; ( ii ) Hierarchy of visual features ( based on FIT ) for an efficient recognition task . Integration of information from the two channels ( " What " and " Where " ) in an iterative feedback mechanism , helps to filter erroneous contents in the outputs of individual modules .
The detection task differs from localization in that there can be any number of objects in each image ( including zero ) , and false positives are penalized by the mean average precision ( mAP ) measure . The localization task is a convenient intermediate step between classification and detection , and allows us to evaluate our localization method independently of challenges specific to detection ( such as learning a background class ) . In Fig .
Fine - Grained Visual Classification ( FGVC ) datasets contain small sample sizes , along with significant intra - class variation and interclass similarity . While prior work has addressed intra - class variation using localization and segmentation techniques , inter - class similarity may also affect feature learning and reduce classification performance . In this work , we address this problem using a novel optimization procedure for the end - to - end neural network training on FGVC tasks .
On Holidays dataset , the Neural codes win the extreme short code category , while off - the - shelf NetVlad performs the best on 256D and higher . We additionally combine MAC and R - MAC with recent localization method for re - ranking to further boost the performance . Our scores compete with state - of - the - art systems based on local features and query expansion .
The loss per example RoI is the sum of a classification log loss that encourages predicting the correct object ( or background ) label and a localization loss that encourages predicting an accurate bounding box ( see for details ) . To share conv network computation between RoIs , SGD mini - batches are created hierarchically .
OverFeat . Sermanet et al . train a convolutional neural network to perform localization and adapt that localizer to perform detection . OverFeat efficiently performs sliding window detection but it is still a disjoint system .
We introduce Complex Shape Histogram , a core component of our artificial intelligence engine , used for classifying 3D point clouds with a Support Vector Machine . We use Complex Shape Histograms also for loop closing detection in the simultaneous localization and mapping algorithm . Our intelligent mobile system is built on top of the Qualitative Spatio-Temporal Representation and Reasoning framework .
In practice , this can require the re-computation of the map when the estimated robot poses change , as when a loop closure is detected . This paper presents a parallelization of a reference implementation of the grid mapping algorithm , which is suitable to be fully run on a graphics card showing huge processing speedups ( up to 50X ) while fully releasing the main processor , which can be very useful for many Simultaneous Localization and Mapping algorithms .
The first signal processor ( 200 ) is electrically connected to the three-dimensional point cloud scanning instrument ( 120 ) and the sensing component ( 110 ) respectively . The first signal processor ( 200 ) is used to update location and status information of a scanning center of the movable scanning device ( 100 ) in real time by means of the simultaneous positioning and mapping algorithm on the basis of data collected by the sensing component ( 110 ) , and is further used to register point cloud data corresponding to any scanning center to a world coordinate system on the basis of real-time location and status information of the scanning center .
Aiming at the high hardware cost of simultaneous positioning and mapping ( SLAM ) for mobile robots , a mobile robot system using LIDAR(Light Detection and Ranging ) for obtaining date observing is designed . The experiments Based on robotic operating system ( ROS ) , using laser radar to acquire 2D laser scanned matching data and an open source GMapping software package for SLAM , RVIZ ( 3D visualization tool for ROS ) is adopted to achieve indoor mapping in an unknown environment .
However , instabilities in sensor position data acquisition remain , because the indoor environment is more complex than the outdoor environment . On the other hand , simultaneous localization and mapping processing is better than indoor positioning for measurement accuracy and sensor cost . However , it is not easy to estimate position data from a single viewpoint directly .
In addition , two levels of sensor data are provided for the purpose of assisting in the complete validation of algorithms using consumer-grade sensors . A forward-facing stereo camera was utilized to capture visual images of the environment and the position information of the vehicle that was estimated through simultaneous localization mapping ( SLAM ) are offered as a baseline . This paper presents 3D map data generated by the SLAM algorithm in the LASer ( LAS ) format for a wide array of research purposes , and a file player and a data viewer have been made available via the Github webpage to allow researchers to conveniently utilize the data in a Robot Operating System ( ROS ) environment .
We present a method to make an accurate and dense reconstruction from the input of video captured by a free moving handheld camera in real time . By the method firstly , the positions of the camera and sparse 3D points are estimated by simultaneous localization mapping . Then the depth maps of selected reference frames are computed from corresponding camera bundles .
In this paper , a normal distribution transform (NDT)-based approach for grid map registration is presented . For simultaneous mapping and localization approaches on laser data , the NDT is widely used to align new laser scans to reference scans . The original grid quantization-based NDT results in good registration performances but has poor convergence properties due to discontinuities of the optimization function and absolute grid resolution .
In recent years , application fields such as industrial automation and indoor robot navigation increased the demand on reliable localization systems . Simultaneous mapping and localization systems often depend on depth imaging in order to reconstruct the scene . Time-of-Flight sensors prove to be well suited for these applications , however are impaired by different error sources .
At the very beginning , the digital image preprocessing methods are used to obtain the static feature point in radar images . Subsequently , the trajectory of the vessel is calculated based on a simultaneous localization and mapping – based algorithm . Finally , the calculated vessel trajectory is compared with the actual trajectory to verify the validity of the proposed approach .
The loss of observability resulting from the use of a single monocular camera is dealt with by constraining the target vehicle to follow ground terrain . An unscented Kalman filter ( UKF ) provides a simultaneous localization and mapping solution for the estimation of aircraft states and feature locations , which define the target 's local environment . Another filter , a loosely coupled Kalman filter for the target states , receives 3D measurements of target position with estimated covariance obtained by an unscented transformation ( UT ) .
3D reconstruction has been well studied based on the multi - view geometry ( MVG ) in the literature . The major research directions include structure from motion ( SfM ) for large - scale high - quality reconstruction and simultaneous localization and mapping ( SLAM ) for navigation . Though they are very successful in these scenarios , they are restricted by 1 ) the coverage that the multiple views can give and 2 ) the appearance of the object that wants to reconstruct .
The system is composed of three central components : tracking , mapping and loop closing which are based on ORB-SLAM2 [ 1 ] . The tracking part estimates the pose of a frame via optimizing both the reprojection error and inverse depth error of matching feature points , rather than just optimizing the reprojection error like ORB-SLAM2 [ 1 ] . The mapping part optimizes the pose of all keyframes and the position of all map points by using bundle adjustment , and an occupancy grid map of the surrounding environment is constructed based on the octree-based mapping framework OctoMap [ 2 ] for high-level tasks , and the map is more useful then the sparse points representation in ORB-SLAM2 [ 1 ] .
To make this system more robust and more rapid , We suggest an improved method to estimate camera-posing , where we use a subset of extracted points for estimation , avoiding mixing large-reprojection-error points in bundle adjustment . The experimental results on the SLAM benchmark dataset KITTI odometry demonstrated that the proposed method outperformed the original implementation of ORB-SLAM2 from the viewpoint of calculation time .
We use multiple triplets of planar segments to generate candidate probability distribution and employ it to find the most probable pose with respect to a global map of planar segments . The PlaneLoc implementation uses the ORB-SLAM2 system that serves as visual odometry and makes it possible to generate observation in a form of sets of local segments online . The proposed approach can be used for global localization with a known map or for loop closing and re-localization in Simultaneous Localization and Mapping .
A dataset of simulated aerial imagery is used for experiments . The experiment results are shown , that the particle filter localization algorithm using the logistic function was able to surpass the accuracy of state-of-the-art ORB-SLAM2 algorithm by 2.6 times . The algorithm is shown to be able to navigate using up-to-date maps more accurately and with an average decrease in precision by 30 % using out-of-date maps .
The robot vision shows excellent measurement performance for obtaining depth information and recognizing unknown object . Additionally , our system is capable of SLAM when tested in a complicated environment by using the well-known ORB-SLAM2 algorithm . The experimental results shows that the average tracking time is ~0.031s , which is below the inverse of the camera frame rate ( 0.033s ) , thus demonstrating a real-time performance .
The state of art Visual SLAM is going from sparse feature to semi-dense feature to provide more information for environment perception , whereas the semi-dense methods often suffer from inaccurate depth map estimation and are easy to become instable for some real-world scenarios . The paper proposes to extend the ORB-SLAM2 framework , which is a robust sparse feature SLAM system tracking camera motion with map maintenance and loop closure , by introducing the unified spherical camera model and the semi-dense depth map . The unified spherical camera model fits the omnidirectional camera well , therefore the proposed Visual SLAM system could handle fisheye cameras which are commonly installed on modern vehicles to provide larger perceiving region .
In this work , we take state-of-the-art patch comparison CNNs , train them from scratch and analyze the performance on the KITTI odometry benchmark . For that , we replace the ORBfrontend within the publicly available ORB-SLAM2 framework through our trained CNN variants and compare both . We show that it is necessary to downsize the complexity of the original architectures to achieve real-time capability .
Porting algorithms originally designed for desktop CPUs on those boards is not straightforward due to hardware limitations . In this paper , we present how we modified and customized the open source SLAM algorithm ORB-SLAM2 to run in real-time on the NVIDIA Jetson TX2 . We adopted a data flow paradigm to process the images , obtaining an efficient CPU/GPU load distribution that results in a processing speed of about 30 frames per second . Quantitative experimental results on four different sequences of the KITTI datasets demonstrate the effectiveness of the proposed approach . The source code of our data flow ORB-SLAM2 algorithm is publicly available on GitHub .
Then , we propose and implement , on top of the Linux operating system , an environment for efficient execution of applications modeled as KPNs . Finally , we perform a simple design space exploration ( DSE ) to investigate the trade-off between system performance and power consumption when alternative ORB-SLAM2 KPNs are executed on different configurations of the ODROID-XU4 platform . The obtained results of this DSE clearly show the feasibility of running ORB-SLAM2 on ODROID-XU4 in real time with a limited power budget for a given range of flying time , thereby enabling cognitive autonomy on small drones .
In order to construct the 3D point cloud map , the color information in the image needs to be transformed and combined with the depth information captured by the stereo camera . In this paper , we construct a binocular SLAM framework called MYNT-vSLAM to improve the sparse mapping of ORB-SLAM2 . Experimental results verified the feasibility of this algorithm , which is robust and fast in complex indoor environment , and can be applied to location and 3D point cloud map construction .
The experimental results show that the proposed method is superior the state-of-the-art methods on both raw LiDAR scans dataset and semi-dense annotation dataset . Furthermore , the errors of the sparse depth produced by stereo ORB-SLAM2 are evaluated and this sparse depth and a single image are fed into the proposed model to further demonstrate the superiority of the proposed work .
Besides , even though there are no moving objects in the frame , there is space for further improvement in accuracy . This article improves the feature point selection based on monocular ORB-SLAM2 system , by creatively using the idea comes from optical flow and then using the K-Means algorithm to classify the matched feature point pairs . The existing open source datasets are used for evaluating the improvement .
Classification component is trained online to guarantee high discriminative power in the presence of distractors . Target estimation is performed by the IoU-predictor network inspired by the IoU-Net which was recently proposed for object detection as an alternative to typical anchor-based bounding box regression techniques . In this work , we further enhance the performance of ATOM by embedding Squeeze-and-Excitation ( SE ) blocks into IoU-Net in ATOM to recalibrate useful features and suppress useless features and obtain ATOMFR . To solve the abnormal changes in the target box in ATOMFR , we add the Relocation Module on ATOMFR and get ATOMFR ( RL ) .
The predicted IoU is used as the detection confidence for NMS . In order to obtain a more accurate IoU prediction , we propose a 3D IoU-Net with IoU sensitive feature learning and an IoU alignment operation . To obtain a perspective-invariant prediction head , we propose an Attentive Corner Aggregation ( ACA ) module by aggregating a local point cloud feature from each perspective of eight corners and adaptively weighting the contribution of each perspective with different attentions .
This makes properly localized bounding boxes degenerate during iterative regression or even suppressed during NMS . In the paper we propose IoU-Net learning to predict the IoU between each detected bounding box and the matched ground-truth . The network acquires this confidence of localization , which improves the NMS procedure by preserving accurately localized bounding boxes .
The Directional Attack on Wireless Localization -or- How to Spoof Your Location with a Tin Can TALS: Trigonometry-based Ad-hoc Localization System for wireless sensor networks Performance Evaluation of Localization Accuracy for a Log-Normal Shadow Fading Wireless Sensor Network under Physical Barrier Attacks Automatic online vision-based inspection system of coupler yoke for freight trains Distributed memory and control VLSI architectures for the 1-D Discrete Wavelet Transform Mac7: Adaptive Smart Toothbrush Evaluating and enhancing google tango localization in indoor environments using fiducial markers Secure localization protocol based on validation node in wireless sensor network A Novel Localization Technique Using Luminous Flux Determination of MS Location through Building Using AoA Method of Frequency 47 GHz Revealing What to Extract from Where, for Object-Centric Content Based Image Retrieval (CBIR) OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks Pairwise Confusion for Fine-Grained Visual Classification CNN Image Retrieval Learns from BoW: Unsupervised Fine-Tuning with Hard Examples Training Region-Based Object Detectors with Online Hard Example Mining You Only Look Once: Unified, Real-Time Object Detection Intelligent Mobile System for Improving Spatial Design Support and Security Inside Buildings Fast Processing of Grid Maps using Graphical Multiprocessors Movable three-dimensional laser scanning system and movable three-dimensional laser scanning method Research and Implementation of SLAM Based on LIDAR for Four-Wheeled Mobile Robot GEOFENCING-BASED LOCALIZATION FOR 3D DATA ACQUISITION NAVIGATION Complex urban dataset with multi-level sensors from highly diverse urban environments Live accurate and dense reconstruction from a handheld camera Clustering improved grid map registration using the normal distribution transform Hybrid Sensing Approach For Coded Modulation Time-of-Flight Cameras A novel positioning approach for an intelligent vessel based on an improved simultaneous localization and mapping algorithm and marine radar Tracking ground targets with measurements obtained from a single monocular camera mounted on an unmanned aerial vehicle Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images A Robust and Accurate Simultaneous Localization and Mapping System for RGB-D Cameras A Rapid Optimization Method for Visual Indirect SLAM Using a Subset of Feature Points PlaneLoc: Probabilistic global localization in 3-D using local planar features Robust GNSS-denied localization for UAV using particle filter and visual odometry A Miniature Stereo Vision System for a Biomimetic Robotic Rat Real-time Omnidirectional Visual SLAM with Semi-Dense Mapping An Approach for CNN-Based Feature Matching Towards Real-Time SLAM Data Flow ORB-SLAM for Real-time Performance on Embedded GPU Boards Enabling Cognitive Autonomy on Small Drones by Efficient On-Board Embedded Computing: An ORB-SLAM2 Case Study Simultaneous Localization and Mapping for Mobile Robots Using Stereo Vision Camera Semi-supervised Depth Estimation from Sparse Depth and a Single Image for Dense Map Construction Improved Monocular ORB-SLAM2 Inspired By The Optical Flow With Better Accuracy Accuracy and Long-Term Tracking via Overlap Maximization Integrated with Motion Continuity 3D IoU-Net: IoU Guided 3D Object Detector for Point Clouds Acquisition of Localization Confidence for Accurate Object Detection
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GLOBECOM IWCMC ICARSC ICVGIP ICLR ECCV ECCV 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Intelligent Vehicles Symposium DATE ICRA ECCV CANDAR Workshops RCAR Intelligent Vehicles Symposium ITSC IROS DSD ICARM ROBIO ICCV Workshops ECCV (14)
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