The autonomous vehicle uses different sensors such as LIDAR , RADAR , Ultrasonic sensors , and cameras for detection of lane lines , objects , traffic sign , traffic lights , and driving path . There are several successful projects on autonomous vehicles , but the main limitations are high costs and regulations . The camera can be a cost-effective solution as compare to LIDAR in all the basic detection needed for autonomous driving . Accurately estimating uncertainties in neural network predictions is of great importance in building trusted DNNs-based models , and there is an increasing interest in providing accurate uncertainty estimation on many tasks , such as security cameras and autonomous driving vehicles . In this paper , we focus on the two main use cases of uncertainty estimation , i.e. , selective prediction and confidence calibration . The authors present cognitive automotive radar ( CARr ) as a significant capability to the sensing technology suite of autonomous driving vehicles . CARr is a closed-loop intelligent radar system utilising the automotive frequency bands of 24 - 25 and 76 - 77 GHz for the purposes of ground vehicle target signature identification . Autonomous road vehicles are increasingly becoming more important and there are several techniques and sensors that are being applied for vehicle control . This paper presents an alternative system for maintaining the position of autonomous vehicles without adding additional elements to the standard sensor architecture , by using a 3D laser scanner for continuously detecting a reference element in situations in which the GNSS receiver fails or provides accuracy below the required level . A precise GNSS ( Global Navigation Satellite System ) localization is vital for autonomous road vehicles , especially in cluttered or urban environments where satellites are occluded , preventing accurate positioning . We propose to fuse GPS ( Global Positioning System ) data with fisheye stereovision to face this problem independently to additional data , possibly outdated , unavailable , and needing correlation with reality . This study is dedicated to solve a collision prevention task for autonomous unmanned aerial vehicles ' ( UAVs ) team by Immune Neuro-Fuzzy Network ( INFN ) application . It is a part of the project aimed at the development of intelligent safety and optimal control systems of autonomous electric vehicles and transport in general . The goal of the current research is to develop the machine learning algorithm for autonomous UAV , that will give possibility for UAVs to train themselves without a teacher to avoid the collisions in the most effective way by changing the UAVs trajectory of the flight and without human intervention , i.e. the system should be self-organized . Wireless charging technology presents perfect fits for autonomous electric vehicles for realizing a fully automated system ( vehicle and charger ) . This paper presents a planning optimization analysis for a pilot project of in-route wireless charging infrastructure serving fixed-route on-demand shared automated electric shuttles ( SAESs ) at Greenville , South Carolina , USA . section : Abstract Semantic image segmentation is an essential component of modern autonomous driving systems , as an accurate understanding of the surrounding scene is crucial to navigation and action planning . Current state - of - the - art approaches in semantic image segmentation rely on pretrained networks that were initially developed for classifying images as a whole . Hand gesture recognition is one of the most natural and intuitive ways to communicate between people and machines , since it closely mimics how human interact with each other . This paper presents a novel method for real-time markerless hand gesture recognition from depth images . First , we make every training sample fuzzy-roughness and use edit nearest neighbor algorithm to remove training sample points in class boundary or overlapping regions , and then use Mountain Clustering method to select representative cluster center points , then Fuzzy-Rough Nearest neighbor algorithm ( FRNN ) is applied to classify the test data . The new algorithm is applied to hand gesture image recognition , the results show that it is more effective and performs better than other nearest neighbor methods . There are two main issues should be considered ; less interactivity in static hand gesture recognition , and less accuracy in dynamic hand gesture recognition . This paper attempts to improve the accuracy of hand-gesture image recognition by experimenting simple deep learning neural network ( DLNN ) . As this work uses a simple DLNN , the relation between the hidden layers is not considered . The goal of this study is to explore the effects of electrode placement on the hand gesture pattern recognition performance . We have conducted experiments with surface EMG sensors using two detecting electrode channels . Moreover , the distance information is used as an additional input parameter to the clustering algorithm , such that the recognition technique will not be vulnerable to distance change . The hand-based gesture recognition proposed in this paper would be a key technology of future automobile user interfaces . Hand gesture recognition is an interesting and challenging study , especially for human interaction with computer . There are many method can used for hand gesture recognizing such as Random K-NN , Tree K-NN , Fuzzy K-NN . K-Nearest Neighbor method is interesting to be examined . Hand gesture system recognition has received great attention in the current few years because of its ability as it is very important for developing an attractive alternative through human computer interaction modalities . However a more natural form of communication system would make the interaction easier , even the performance would increase and additional cost for hardware would also decrease in fact we might not need extra hardware as well . Hand human gesture recognition has been an important research topic widely studied around the world , as this field offers the ability to identify , recognize , and analyze human gestures in order to control devices or to interact with computer interfaces . In particular , in medical training , this approach is an important tool that can be used to obtain an objective evaluation of a procedure performance . State - of - the art approaches in this field mostly rely on hand - crafted features leaving a lot of potential for higher accuracies . In contrast to other fields such as face or gesture recognition , only very few works on deep learning applied to facial expression recognition have been reported so far in which the architecture parameters are fixed . We are not aware of publications in which the architecture of a deep neural network for facial expression recognition is subject to extensive model selection . Modeling objects via their axes of symmetry , and in particular , using skeletons , has a long history in computer vision . Skeletonization algorithms provide a concise and effective representation of deformable objects , while supporting many applications , including object recognition and retrieval , pose estimation , hand gesture recognition , shape matching , scene text detection , and road detection in aerial scenes . Early algorithms for computing skeletons directly from images yield a gradient intensity map , driven by geometric constraints between skeletal pixels and edge fragments . In contrast to these approaches , this work addresses the problem of vehicle perception using a single 3D laser scanner . First contribution of this research is made by the extension of a generic 3D mapping framework based on an optimized occupancy grid representation to solve the problem of simultaneous localization and mapping ( SLAM ) . Using the 3D occupancy grid , we introduce a variance-based elevation map for the segmentation of range measurements corresponding to the ground . In recent years , dense visual SLAM ( Simultaneous Localization And Mapping ) has been proved that it has lots of advantages on the accuracy of pose estimation compared to sparse features due to explo ... Rao-Blackwellized particle filters have become a popular tool to solve the simultaneous localization and mapping problem . This technique applies a particle filter in which each particle carries an ... zielgerichtet selbststandig navigieren zu konnen . Das Simultaneous Localization and Mapping Problem , beziehungsweise Teilprobleme davon , werden , je nach verwendeter Sensorik , auch als Bundelausgleichung , Structure from Motion oder SLAM bezeichnet . How to improve the real-time performance of the system and reduce the cost of equipment is the key issue . In order to deal with the problem , we propose an improved scheme , which uses LOAM algorithm to realize the simultaneous location and mapping , saving the use of the expensive GPS/IMU system . Besides we propose a cloud classification , a power line fitting and a safety distance detection method . We show how to recover 2D structure and motion linearly in order to initialize simultaneous mapping and localization ( SLAM ) for bearings-only measurements and planar motion . The method supplies a good initial estimate of the geometry , even without odometry or in multiple robot scenarios . Simultaneous mapping and localization ( SLAM ) in an real indoor environment is still a challenging task . Traditional SLAM approaches rely heavily on low-level geometric constraints like corners or lines , which may lead to tracking failure in textureless surroundings or cluttered world with dynamic objects . Robot navigation in unknown environments requires an efficient exploration method . Exploration involves not only to determine towards the robot must to move but also motion planning , and simultaneous localization and mapping processes . The final goal of the exploration task is to build a map of the environment that previously the robot did n’t know . This paper discusses the use of three-dimensional ( 3-D ) corner features extracted from a 2-D laser scanner as landmarks in a simultaneous localization and mapping solution . The corner landmarks can be identified and tracked using their angles , and the observations of the corners are directly used to correct attitude errors . In this paper , we propose a robust incremental framework for loop closure detection , termed incremental loop closure verification . Our approach reformulates the problem of loop closure detection as an instance of a multi-model hypothesize-and-verify framework , in which multiple loop closure hypotheses are generated and verified in terms of the consistency between loop closure hypotheses and VPR constraints at multiple viewpoints along the robot 's trajectory . Furthermore , we consider the general incremental setting of loop closure detection , in which the system must update both the set of VPR constraints and that of loop closure hypotheses when new constraints or hypotheses arrive during robot navigation . The same structure also allows us to create a highly-effective search tool for large-scale videos , which we demonstrate in this paper . We show the efficiency of proposed approaches for retrieval and loop closure on standard datasets , and on a large-scale video from a mobile camera . While a well-calibrated camera rig has a fixed and known scale , camera trajectories produced by monocular motion estimation necessarily lack a scale estimate . Thus , when performing loop closure in monocular visual odometry , or registering separate structure-from-motion reconstructions , we must estimate a seven degree-of-freedom similarity transform from corresponding observations . Existing approaches solve this problem , in specialized configurations , by aligning 3D triangulated points or individual camera pose estimates . When GPS localization fails in some scenes such as very messy environments and severe signal occlusion , we can consider using visual-based localization to obtain the pose of the query images . Visual-based localization ( VBL ) has been widely used in the field of visual tasks , such as augmented reality , unmanned vehicle navigation , robotics , closed-loop detection , SFM ( Structure from Motion ) models . After years of development , the methods of visual-based localization ( VBL ) have been enriched and developed , In order to better understand the latest developments in VBL , overall research status and possible future development trends , we need make a systematic detailed classification of VBL . Effect of quadrature error on MEMS gyroscope performance is inhibited and the input angular rate signal is extracted by using the method of correlation detection . Simulink is used on the modeling and simulation of gyroscope , the simulation results show that the effect of quadrature error can be eliminated effectively under the condition of open loop detection and closed loop detection . Hence , the validity of the theoretical analysis is verified . SLAM is a fundamental problem in robotics and loop closure detection is an important part of SLAM . This paper attempts to use supervised learning methods to solve the loop closure detection problem in vision SLAM . We proposed Siamese-ResNet network , which combines Siamese network with ResNet to detect loop closure . This filtering scheme avoids issues linked with the classical extended Kalman filtering techniques such as linearization errors , initialization problems , and noise statistics assumptions . Finally , a reliable map management approach , based on the k-nearest landmark concept , is presented to allow efficient loop closing detection and map building . This approach reduces significantly the complexity of the airborne VSLAM algorithm by making it independent of the landmark number . The system defines a Complex Shape Histogram and an artificial intelligence engine , used for classifying 3D point clouds with Support VectorMachine . The Complex Shape Histograms is used for loop closing detection in the simultaneous localization and mapping algorithm . The proposed system was tested in the Museum of the History of Polish Jews ( Warsaw , Poland ) , and Airport in Łódź , Poland . The loop closure detection ( LCD ) is an essential part of visual simultaneous localization and mapping systems ( SLAM ) . LCD is capable of identifying and compensating the accumulation drift of localization algorithms to produce an consistent map if the loops are checked correctly . We create a virtual laser scan from the depth camera , which provides depth image to be converted into 2D laser scan . Meanwhile , the color image provided by the depth camera could be used to extract feature descriptions which will be used in the loop closure process . The depth camera act as a low cost laser scanner and a RGB camera in this case .
The autonomous vehicle uses different sensors such as LIDAR , RADAR , Ultrasonic sensors , and cameras for detection of lane lines , objects , traffic sign , traffic lights , and driving path . There are several successful projects on autonomous vehicles , but the main limitations are high costs and regulations . The camera can be a cost-effective solution as compare to LIDAR in all the basic detection needed for autonomous driving .
Accurately estimating uncertainties in neural network predictions is of great importance in building trusted DNNs-based models , and there is an increasing interest in providing accurate uncertainty estimation on many tasks , such as security cameras and autonomous driving vehicles . In this paper , we focus on the two main use cases of uncertainty estimation , i.e. , selective prediction and confidence calibration .
The authors present cognitive automotive radar ( CARr ) as a significant capability to the sensing technology suite of autonomous driving vehicles . CARr is a closed-loop intelligent radar system utilising the automotive frequency bands of 24 - 25 and 76 - 77 GHz for the purposes of ground vehicle target signature identification .
Autonomous road vehicles are increasingly becoming more important and there are several techniques and sensors that are being applied for vehicle control . This paper presents an alternative system for maintaining the position of autonomous vehicles without adding additional elements to the standard sensor architecture , by using a 3D laser scanner for continuously detecting a reference element in situations in which the GNSS receiver fails or provides accuracy below the required level .
A precise GNSS ( Global Navigation Satellite System ) localization is vital for autonomous road vehicles , especially in cluttered or urban environments where satellites are occluded , preventing accurate positioning . We propose to fuse GPS ( Global Positioning System ) data with fisheye stereovision to face this problem independently to additional data , possibly outdated , unavailable , and needing correlation with reality .
This study is dedicated to solve a collision prevention task for autonomous unmanned aerial vehicles ' ( UAVs ) team by Immune Neuro-Fuzzy Network ( INFN ) application . It is a part of the project aimed at the development of intelligent safety and optimal control systems of autonomous electric vehicles and transport in general . The goal of the current research is to develop the machine learning algorithm for autonomous UAV , that will give possibility for UAVs to train themselves without a teacher to avoid the collisions in the most effective way by changing the UAVs trajectory of the flight and without human intervention , i.e. the system should be self-organized .
Wireless charging technology presents perfect fits for autonomous electric vehicles for realizing a fully automated system ( vehicle and charger ) . This paper presents a planning optimization analysis for a pilot project of in-route wireless charging infrastructure serving fixed-route on-demand shared automated electric shuttles ( SAESs ) at Greenville , South Carolina , USA .
section : Abstract Semantic image segmentation is an essential component of modern autonomous driving systems , as an accurate understanding of the surrounding scene is crucial to navigation and action planning . Current state - of - the - art approaches in semantic image segmentation rely on pretrained networks that were initially developed for classifying images as a whole .
Hand gesture recognition is one of the most natural and intuitive ways to communicate between people and machines , since it closely mimics how human interact with each other . This paper presents a novel method for real-time markerless hand gesture recognition from depth images .
First , we make every training sample fuzzy-roughness and use edit nearest neighbor algorithm to remove training sample points in class boundary or overlapping regions , and then use Mountain Clustering method to select representative cluster center points , then Fuzzy-Rough Nearest neighbor algorithm ( FRNN ) is applied to classify the test data . The new algorithm is applied to hand gesture image recognition , the results show that it is more effective and performs better than other nearest neighbor methods .
There are two main issues should be considered ; less interactivity in static hand gesture recognition , and less accuracy in dynamic hand gesture recognition . This paper attempts to improve the accuracy of hand-gesture image recognition by experimenting simple deep learning neural network ( DLNN ) . As this work uses a simple DLNN , the relation between the hidden layers is not considered .
The goal of this study is to explore the effects of electrode placement on the hand gesture pattern recognition performance . We have conducted experiments with surface EMG sensors using two detecting electrode channels .
Moreover , the distance information is used as an additional input parameter to the clustering algorithm , such that the recognition technique will not be vulnerable to distance change . The hand-based gesture recognition proposed in this paper would be a key technology of future automobile user interfaces .
Hand gesture recognition is an interesting and challenging study , especially for human interaction with computer . There are many method can used for hand gesture recognizing such as Random K-NN , Tree K-NN , Fuzzy K-NN . K-Nearest Neighbor method is interesting to be examined .
Hand gesture system recognition has received great attention in the current few years because of its ability as it is very important for developing an attractive alternative through human computer interaction modalities . However a more natural form of communication system would make the interaction easier , even the performance would increase and additional cost for hardware would also decrease in fact we might not need extra hardware as well .
Hand human gesture recognition has been an important research topic widely studied around the world , as this field offers the ability to identify , recognize , and analyze human gestures in order to control devices or to interact with computer interfaces . In particular , in medical training , this approach is an important tool that can be used to obtain an objective evaluation of a procedure performance .
State - of - the art approaches in this field mostly rely on hand - crafted features leaving a lot of potential for higher accuracies . In contrast to other fields such as face or gesture recognition , only very few works on deep learning applied to facial expression recognition have been reported so far in which the architecture parameters are fixed . We are not aware of publications in which the architecture of a deep neural network for facial expression recognition is subject to extensive model selection .
Modeling objects via their axes of symmetry , and in particular , using skeletons , has a long history in computer vision . Skeletonization algorithms provide a concise and effective representation of deformable objects , while supporting many applications , including object recognition and retrieval , pose estimation , hand gesture recognition , shape matching , scene text detection , and road detection in aerial scenes . Early algorithms for computing skeletons directly from images yield a gradient intensity map , driven by geometric constraints between skeletal pixels and edge fragments .
In contrast to these approaches , this work addresses the problem of vehicle perception using a single 3D laser scanner . First contribution of this research is made by the extension of a generic 3D mapping framework based on an optimized occupancy grid representation to solve the problem of simultaneous localization and mapping ( SLAM ) . Using the 3D occupancy grid , we introduce a variance-based elevation map for the segmentation of range measurements corresponding to the ground .
In recent years , dense visual SLAM ( Simultaneous Localization And Mapping ) has been proved that it has lots of advantages on the accuracy of pose estimation compared to sparse features due to explo ...
Rao-Blackwellized particle filters have become a popular tool to solve the simultaneous localization and mapping problem . This technique applies a particle filter in which each particle carries an ...
zielgerichtet selbststandig navigieren zu konnen . Das Simultaneous Localization and Mapping Problem , beziehungsweise Teilprobleme davon , werden , je nach verwendeter Sensorik , auch als Bundelausgleichung , Structure from Motion oder SLAM bezeichnet .
How to improve the real-time performance of the system and reduce the cost of equipment is the key issue . In order to deal with the problem , we propose an improved scheme , which uses LOAM algorithm to realize the simultaneous location and mapping , saving the use of the expensive GPS/IMU system . Besides we propose a cloud classification , a power line fitting and a safety distance detection method .
We show how to recover 2D structure and motion linearly in order to initialize simultaneous mapping and localization ( SLAM ) for bearings-only measurements and planar motion . The method supplies a good initial estimate of the geometry , even without odometry or in multiple robot scenarios .
Simultaneous mapping and localization ( SLAM ) in an real indoor environment is still a challenging task . Traditional SLAM approaches rely heavily on low-level geometric constraints like corners or lines , which may lead to tracking failure in textureless surroundings or cluttered world with dynamic objects .
Robot navigation in unknown environments requires an efficient exploration method . Exploration involves not only to determine towards the robot must to move but also motion planning , and simultaneous localization and mapping processes . The final goal of the exploration task is to build a map of the environment that previously the robot did n’t know .
This paper discusses the use of three-dimensional ( 3-D ) corner features extracted from a 2-D laser scanner as landmarks in a simultaneous localization and mapping solution . The corner landmarks can be identified and tracked using their angles , and the observations of the corners are directly used to correct attitude errors .
In this paper , we propose a robust incremental framework for loop closure detection , termed incremental loop closure verification . Our approach reformulates the problem of loop closure detection as an instance of a multi-model hypothesize-and-verify framework , in which multiple loop closure hypotheses are generated and verified in terms of the consistency between loop closure hypotheses and VPR constraints at multiple viewpoints along the robot 's trajectory . Furthermore , we consider the general incremental setting of loop closure detection , in which the system must update both the set of VPR constraints and that of loop closure hypotheses when new constraints or hypotheses arrive during robot navigation .
The same structure also allows us to create a highly-effective search tool for large-scale videos , which we demonstrate in this paper . We show the efficiency of proposed approaches for retrieval and loop closure on standard datasets , and on a large-scale video from a mobile camera .
While a well-calibrated camera rig has a fixed and known scale , camera trajectories produced by monocular motion estimation necessarily lack a scale estimate . Thus , when performing loop closure in monocular visual odometry , or registering separate structure-from-motion reconstructions , we must estimate a seven degree-of-freedom similarity transform from corresponding observations . Existing approaches solve this problem , in specialized configurations , by aligning 3D triangulated points or individual camera pose estimates .
When GPS localization fails in some scenes such as very messy environments and severe signal occlusion , we can consider using visual-based localization to obtain the pose of the query images . Visual-based localization ( VBL ) has been widely used in the field of visual tasks , such as augmented reality , unmanned vehicle navigation , robotics , closed-loop detection , SFM ( Structure from Motion ) models . After years of development , the methods of visual-based localization ( VBL ) have been enriched and developed , In order to better understand the latest developments in VBL , overall research status and possible future development trends , we need make a systematic detailed classification of VBL .
Effect of quadrature error on MEMS gyroscope performance is inhibited and the input angular rate signal is extracted by using the method of correlation detection . Simulink is used on the modeling and simulation of gyroscope , the simulation results show that the effect of quadrature error can be eliminated effectively under the condition of open loop detection and closed loop detection . Hence , the validity of the theoretical analysis is verified .
SLAM is a fundamental problem in robotics and loop closure detection is an important part of SLAM . This paper attempts to use supervised learning methods to solve the loop closure detection problem in vision SLAM . We proposed Siamese-ResNet network , which combines Siamese network with ResNet to detect loop closure .
This filtering scheme avoids issues linked with the classical extended Kalman filtering techniques such as linearization errors , initialization problems , and noise statistics assumptions . Finally , a reliable map management approach , based on the k-nearest landmark concept , is presented to allow efficient loop closing detection and map building . This approach reduces significantly the complexity of the airborne VSLAM algorithm by making it independent of the landmark number .
The system defines a Complex Shape Histogram and an artificial intelligence engine , used for classifying 3D point clouds with Support VectorMachine . The Complex Shape Histograms is used for loop closing detection in the simultaneous localization and mapping algorithm . The proposed system was tested in the Museum of the History of Polish Jews ( Warsaw , Poland ) , and Airport in Łódź , Poland .
The loop closure detection ( LCD ) is an essential part of visual simultaneous localization and mapping systems ( SLAM ) . LCD is capable of identifying and compensating the accumulation drift of localization algorithms to produce an consistent map if the loops are checked correctly .
We create a virtual laser scan from the depth camera , which provides depth image to be converted into 2D laser scan . Meanwhile , the color image provided by the depth camera could be used to extract feature descriptions which will be used in the loop closure process . The depth camera act as a low cost laser scanner and a RGB camera in this case .
Monocular Camera based Computer Vision System for Cost Effective Autonomous Vehicle Revisiting the Evaluation of Uncertainty Estimation and Its Application to Explore Model Complexity-Uncertainty Trade-Off Ground vehicle target signature identification with cognitive automotive radar using 24–25 and 76–77 GHz bands Improving the Lane Reference Detection for Autonomous Road Vehicle Control Fisheye-Based Method for GPS Localization Improvement in Unknown Semi-Obstructed Areas Machine learning algorithm of immune neuro-fuzzy anti-collision embedded system for autonomous unmanned aerial vehicles team Optimum Planning for Inductively Charged On-demand Automated Electric Shuttles at Greenville, South Carolina Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes Real-time Hand Gesture Recognition from Depth Images Using Convex Shape Decomposition Method Fuzzy-Rough Set Based Nearest Neighbor Clustering Classification Algorithm Experimenting Hand-Gesture Image Recognition using Simple Deep Neural Network Research on Gesture Definition and Electrode Placement in Pattern Recognition of Hand Gesture Action SEMG Hand-Based Gesture Recognition for Vehicular Applications Using IR-UWB Radar Alphabet Sign Language Recognition Using K-Nearest Neighbor Optimization A Survey on Dynamic Hand Gesture for Number Recognition Automatic gesture analysis using constant affine velocity Deep Learning For Smile Recognition DeepFlux for Skeletons in the Wild 3D Perception of Outdoor and Dynamic Environment using Laser Scanner. (Perception 3D de l'environnement extérieur et dynamique utilisant Laser Scanner) Local optimized and scalable frame-to-model SLAM Fast and accurate SLAM with Rao-Blackwellized particle filters Simultaneous Localization and Mapping Real-time Power Line Safety Distance Detection System Based on LOAM Slam Linear 2D localization and mapping for single and multiple robot scenarios Robust Data Association for Object-level Semantic SLAM Exploration of 2D and 3D Environments using Voronoi Transform and Fast Marching Method Attitude Correction Using Corner Landmarks in 3-D SLAM Multi-model hypothesize-and-verify approach for incremental loop closure verification Coresets for visual summarization with applications to loop closure A Minimal Solution to the Generalized Pose-and-Scale Problem A review of Visual-Based Localization Analysis and simulation of quadrature error on MEMS gyroscope Siamese-ResNet: Implementing Loop Closure Detection based on Siamese Network Experimental airborne NH∞ vision-based simultaneous localization and mapping in unknown environments Editorial for Advances in Intelligent Mobile Applications Special Issue Matching-range-constrained real-time loop closure detection with CNNs features An approach to restaurant service robot SLAM
c3abd72ef556c535bc86fc0dc190cffeb2878182 065cc8221536a259221ded0a45bbe6d1fd5e528d a015e50f38f80d76ea0a3bbc3594a411bd1f62f5 85b5f58ef990a032f33c5f66597edbe6232d5da5 d769d142b8514c5706e2757e111dc9b178a50af6 eb96733e13eb19057bfe44080a8093a2a0a57417 3c67acaf4d7180a4621a81baff0414b1d9fc77ff adbe39ed689de1d83d2bfad739e82470bb27404b 95a5d2223d944f361111a97711718bff81f4ab6b a47c0ba1c26dc169149c6f119460413d9658a66e 83485e45db88e587e5d9d9ed5720b694ef91c497 5ce6dc0704a9f430bda5388abeb7068ac6a71e0a cf4f95856a25bda715aaed9045e101785564c272 51ee0833ce847a0e3f1cd1af865d03d4956102ef 46b6ee247c18eb8c0d29fe2a4802baca75922401 d5fd6dc4eb03e1a658a641199b29cd9fa8b3dc07 3c7c5facca17842b88b9b45947c97801e76c45cc 4ac166596505716e1d069f2207c8aa6905de8fa7 345311f475d2f5d66f2fe0e183bbcd46bb729c45 48596c399197184918347ebb0504c33612e1cc31 5c87737b4f22be6c15cfede2ec60cd57f6eed84c a6a0804e9e67bfa818d7d1e8fe2095f203352603 f6c2aa6a502bc13145d6e1301eef89c23b35e3a1 9469131fb934622aad3563e700161f7503ff13f6 e268a159fb5cc776b9a1b9c512af7505f8b87e53 1f0e2c0ae4994c1acea8407c5544d41f96d53b7c 152213bc5b226bcd8674a70fffc449d6d6ab86ac 50d4cd2be2cfd00e50bb33af0af980a13969e39a 75fad63095b8fab84c94052ab56b5ea04e76b787 4405ec108ab7f29cbf447141688a655262b3f8dd c9ab073660c34054232d96131127ac34af7787ca b0c558fe2575f633dbbd83c60d2c21a1a14146b1 26405cab4b79eb0ecbcfa260877b12f8efb6bbb3 2ed00fc08e02f6f97962af15f9ed6d7181d7abde
["Computer Science"] ["Computer Science","Mathematics"] ["Mathematics"] ["Engineering","Computer Science"] ["Engineering","Computer Science","Medicine"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Medicine","Engineering","Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science","Medicine"] ["Computer Science","Geography"] ["Computer Science"] ["Computer Science"] ["Physics","Computer Science"] ["Computer Science"] ["Computer Science","Mathematics"] ["Engineering","Computer Science"] ["Computer Science","Engineering"] ["Computer Science","Mathematics"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science","Engineering"]
2019 2019 2018 2016 2017 2019 2019 2017 2014 2005 2018 2008 2017 2019 2016 2014 2016 2019 2013 2016 2007 2007 2018 2002 2019 2009 2018 2016 2015 2014 2019 2008 2018 2010 2016 2016 2016
ICCCNT APPIS IAS 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) FSKD (1) ICMB EMBC ArXiv 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Robotics and Cognitive Approaches to Spatial Mapping ICRA ICRA CVPR Intelligent Vehicles Symposium ROBIO
https://api.semanticscholar.org/c3abd72ef556c535bc86fc0dc190cffeb2878182 https://api.semanticscholar.org/065cc8221536a259221ded0a45bbe6d1fd5e528d https://api.semanticscholar.org/a015e50f38f80d76ea0a3bbc3594a411bd1f62f5 https://api.semanticscholar.org/85b5f58ef990a032f33c5f66597edbe6232d5da5 https://api.semanticscholar.org/d769d142b8514c5706e2757e111dc9b178a50af6 https://api.semanticscholar.org/eb96733e13eb19057bfe44080a8093a2a0a57417 https://api.semanticscholar.org/3c67acaf4d7180a4621a81baff0414b1d9fc77ff https://www.semanticscholar.org/paper/1bb5081766d84549b3411cb56e60483c33cca52d https://api.semanticscholar.org/adbe39ed689de1d83d2bfad739e82470bb27404b https://api.semanticscholar.org/95a5d2223d944f361111a97711718bff81f4ab6b https://api.semanticscholar.org/a47c0ba1c26dc169149c6f119460413d9658a66e https://api.semanticscholar.org/83485e45db88e587e5d9d9ed5720b694ef91c497 https://api.semanticscholar.org/5ce6dc0704a9f430bda5388abeb7068ac6a71e0a https://api.semanticscholar.org/cf4f95856a25bda715aaed9045e101785564c272 https://api.semanticscholar.org/51ee0833ce847a0e3f1cd1af865d03d4956102ef https://api.semanticscholar.org/46b6ee247c18eb8c0d29fe2a4802baca75922401 https://www.semanticscholar.org/paper/8494fa7b95969d8c0df20ba20083b7e5cf73ebe3 https://www.semanticscholar.org/paper/9431bd6b37b3aced0b4bddaa1800a3341ccf47f1 https://api.semanticscholar.org/d5fd6dc4eb03e1a658a641199b29cd9fa8b3dc07 https://api.semanticscholar.org/3c7c5facca17842b88b9b45947c97801e76c45cc https://api.semanticscholar.org/4ac166596505716e1d069f2207c8aa6905de8fa7 https://api.semanticscholar.org/345311f475d2f5d66f2fe0e183bbcd46bb729c45 https://api.semanticscholar.org/48596c399197184918347ebb0504c33612e1cc31 https://api.semanticscholar.org/5c87737b4f22be6c15cfede2ec60cd57f6eed84c https://api.semanticscholar.org/a6a0804e9e67bfa818d7d1e8fe2095f203352603 https://api.semanticscholar.org/f6c2aa6a502bc13145d6e1301eef89c23b35e3a1 https://api.semanticscholar.org/9469131fb934622aad3563e700161f7503ff13f6 https://api.semanticscholar.org/e268a159fb5cc776b9a1b9c512af7505f8b87e53 https://api.semanticscholar.org/1f0e2c0ae4994c1acea8407c5544d41f96d53b7c https://api.semanticscholar.org/152213bc5b226bcd8674a70fffc449d6d6ab86ac https://api.semanticscholar.org/50d4cd2be2cfd00e50bb33af0af980a13969e39a https://api.semanticscholar.org/75fad63095b8fab84c94052ab56b5ea04e76b787 https://api.semanticscholar.org/4405ec108ab7f29cbf447141688a655262b3f8dd https://api.semanticscholar.org/c9ab073660c34054232d96131127ac34af7787ca https://api.semanticscholar.org/b0c558fe2575f633dbbd83c60d2c21a1a14146b1 https://api.semanticscholar.org/26405cab4b79eb0ecbcfa260877b12f8efb6bbb3 https://api.semanticscholar.org/2ed00fc08e02f6f97962af15f9ed6d7181d7abde