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# PROB-SLAM: Real-time Visual SLAM Based on Probabilistic Graph Optimization

Xianwei Meng  Bonian Li

###### Abstract

Traditional SLAM algorithms are typically based on artificial features, which
lack high-level information. By introducing semantic information, SLAM can own
higher stability and robustness rather than purely hand-crafted features.
However, the high uncertainty of semantic detection networks prohibits the
practical functionality of high-level information. To solve the uncertainty
property introduced by semantics, this paper proposed a novel probability map
based on the Gaussian distribution assumption. This map transforms the
semantic binary object detection into probability results, which help
establish a probabilistic data association between artificial features and
semantic info. Through our algorithm, the higher confidence will be given
higher weights in each update step while the edge of the detection area will
be endowed with lower confidence. Then the uncertainty is undermined and has
less effect on nonlinear optimization. The experiments are carried out in the
TUM RGBD dataset, results show that our system improves ORB-SLAM2 by about 15%
in indoor environments’ errors. We have demonstrated that the method can be
successfully applied to environments containing dynamic objects.

###### keywords:

SLAM, semantics, object detection, probability map

##  1 INTRODUCTION

Over the past few decades, with the development of science and technology, the
use of computers for simultaneous localization and mapping (SLAM) has become
an indispensable part of many automation fields. It enables the robot to have
the ability to locate and obtain the location information of the environment
in a new environment, so it is a crucial technology for autonomous navigation
of mobile robots[1] and is widely used in autonomous driving[2], drones and
augmented reality (AR) [3] and other fields.

In the past few decades, the SLAM problem has been a widely used research
direction, so many excellent visual SLAM systems have emerged, such as
ORBSLAM[4], ORB-SLAM2[5], LSD-SLAM[6], and SVO[7]. When the environment is
static, or there are few dynamic elements, these excellent systems can achieve
satisfactory performance. However, unstable environmental factors are
ubiquitous, and sometimes the movement of people in indoor positioning will
affect the positioning results. Huge destruction. Due to static world
assumptions and lighting or other influences, the above standard SLAM systems
are significantly less accurate and less robust in such highly dynamic
environments.

An important branch of SALM, dynamic SLAM algorithms, can be a hot research
area today. This technology can already be used in many real-world
applications, such as mobile robotics. For traditional dynamic SLAM
algorithms, Cadena et al. provide a more comprehensive overview of the past,
present, and future of SLAM[8]. However, this type of dynamic SLAM has no
prior semantic information. In the absence of semantic information as a
priori, using reliable constraints to find the right feature matching
relationship is the primary method to deal with dynamic SLAM problems. Li et
al. proposed a static weighting method for keyframe edge points and integrated
it into the IAICP method to reduce tracking error[9] Jin et al. proposed a
dense visual odometry calculation method based on a background model,
estimated from the depth scene Nonparametric Background Model[10]. Dai et al.
distinguish dynamic and static map points based on feature correlation[11].
Flow fusion uses optical flow residuals to highlight dynamic regions in RGB-D
point clouds[12]. Since deep learning networks do not need to provide semantic
priors, the above methods are generally fast in handling dynamic factors but
lack accuracy. In response to this problem, VSO[13] proposed a novel method.
If the probability of each object in the picture is obtained based on the
semantic segmentation of a particular frame, that is, the probability of using
the object is inversely proportional to the distance from the center of the
object, The concept of distance can be used to create the concept of
normalization, which reduces the background part that is far away in semantic
segmentation, thereby improving the accuracy of pose estimation, but because
the error of semantic segmentation itself is more prominent, and semantic
segmentation often There are gaps between different instances, resulting in
lower overall accuracy, so this method is not very helpful for practical
results.

Specifically, in deep learning methods, the most common techniques in the deep
learning section[14] are usually adopted to generate the necessary semantic
information, such as object detection and semantic segmentation techniques. Li
et al. used the well-known semantic segmentation network SegNet to segment
images, which were further processed to distinguish dynamic objects[15]. Zhang
et al. adopted YOLO[16] to identify objects in the environment and build a
semantic map to filter dynamic feature points to improve the system’s
accuracy. Liu et al. adopted different semantic segmentation methods to detect
dynamic objects and remove outliers[17]. Deep learning methods are helpful
when dynamic objects in the environment are known in advance. However, deep
learning methods rely heavily on the quality of the network[18]. A network
with a simple architecture may be unable to effectively identify dynamic
objects in some cases, while a complex network architecture may slow down the
system.

A subset of SLAM also applies CNNs to segment previously dynamic objects in
images and re-examine them using multi-view geometric models. Zhao et al. [19]
adopted a Mask-RCNN network to discover the contours of latent dynamic
objects. They then checked the consistency of optical flow to detect the
actual state of dynamic regions. Yang et al. [20] first used an object
detection model to detect predefined dynamic objects and then adopted multi-
view constraints to confirm dynamic pixel depth images. However, semantic
segmentation, such as SegNet and Mask-RCNN, is time-consuming, and multi-view
methods require multiple frames of images. This makes the SLAM system face
practical and stability challenges in practical applications.

In detail, SLAM generally has two basic requirements: robustness of tracking
and real-time performance. Therefore, detecting dynamic objects that populate
the scene and preventing tracking algorithms from using data associations
related to these dynamic objects in real-time is a challenge that enables SLAM
to be deployed in the real world. With the development of deep learning
technology, many studies have introduced semantic information as a constraint
to solve dynamic problems in SLAM. By means of object recognition and semantic
segmentation, constraints are added to the feature point information
distributed on objects. SLAM performance tends to be significantly improved.
First, Ryan R et al. proposed the concept of probabilistic semantics. They
added geometry, semantics, and sensors into the same framework for
optimization and solved it with the EM algorithm, realizing a SLAM system with
higher positioning accuracy. The sensor and the lens are used to observe the
correspondence between the road signs and the road signs, and the weight
factor w represents it. The probability model calculates the object’s center,
and then the second image projection is performed to approach the center of
the detection frame. Konstantinos-Nektarios Lianos et al. [13] proposed a VSO
algorithm. They proposed to use semantic segmentation to obtain the
probability of each object in the picture. However, the boundary of semantic
segmentation often lacked accuracy, and the real-time performance was also
poor. Poor and these defects are more significant when there are dynamic
objects in the scene, so the practice of introducing semantic information into
SLAM cannot be used in most scenes with dynamic objects.

In response to the above problems, we tightly organize deep learning and
static SLAM. At the same time, the two-dimensional Gaussian distribution is
used to process the probability distribution map, which effectively reduces
the negative impact of inaccurate semantic segmentation borders, realizes the
filtering of feature points distributed on dynamic objects, and retains
sufficient information on static objects to the greatest extent. Feature
points, to adjust the weight of feature points distributed on dynamic objects
in optimization so that the dynamic SLAM system is more stable and less
affected by moving objects - we propose PROB-SLAM, which is based on a High-
performance, visual SLAM system for object detection and probability
distribution maps in dynamic indoor environments. Based on ORB-SLAM2[5], this
Prob-SLAM system uses YOLO[16] to identify dynamic objects based on predefined
knowledge, obtains semantic information through YOLOv5, and distinguishes
high-confidence regions from the boundary range obtained by semantic
segmentation. In the low-confidence region, Gaussian distribution is
introduced into the high-confidence region, a probability distribution map is
finally constructed, SLAM is optimized from a new dimension, and the attribute
of confidence of critical points is given according to the probability
distribution map under various constraints. Next, calculate the feature point
information required for pose estimation. The confidence of the critical
points is used as a weight to participate in the camera pose optimization. In
this way, the goal of reducing the importance of dynamic objects in SLAM and
retaining the information of static objects to the greatest extent is
achieved, which makes good use of semantic information and reduces the
negative impact of inaccurate semantic segmentation. To verify the
effectiveness of PROB-SLAM, we conduct dynamic monocular SLAM experiments on
the TUM dataset. Our method achieves higher-than-original localization
accuracy in both low-dynamic and high-dynamic scenes on the same dataset
compared with the original ORB-SLAM. The main contributions of this paper are
as follows:

1) A probabilistic algorithm based on the foreground (within the bounding box)
and background (outside the bounding box) information is attached to the key
points. Aiming at the error problem caused by dynamic objects, a PROB
algorithm based on semantic segmentation is proposed to weaken the influence
of dynamic objects and preserve the influence of static objects.

2) Based on the YOLOv5 target detection network and predefined knowledge,
distinguish between dynamic objects and static objects, distinguish the
picture frame’s foreground and background points, and perform a probability
algorithm based on two-dimensional Gaussian distribution for the foreground
points to minimize the inaccurate boundary of semantic segmentation. With the
coming influence, the final construction gets the probability distribution
map.

3) The probability distribution map constructed according to the target motion
attribute weakens the optimization weight of the dynamic object, provides it
to the subsequent optimization algorithm and processes it in the subsequent
newly added PROB thread, and uses the probability distribution map to adjust
the covariance matrix of each error term in the nonlinear optimization
process, to improve the robustness and adaptability of SLAM systems.

##  2 Proposed Method

This section will detail the overall architecture of PROB-SLAM proposed for
dynamic environments. It consists of three threads running in parallel: the
object detection thread, the probability distribution map processing thread,
and the improvement tracking thread. The object detection thread is
responsible for scene semantic segmentation, the probability distribution map
processing thread is responsible for the construction of the confidence
distribution map, and the improved tracking thread is responsible for the
construction of the BA problem algorithm based on the confidence distribution
map:

---
Figure 1:  RGB images are processed by two independent threads. In the first
thread, RGB images are directly subjected to feature extraction, while in the
second thread, RGB images are first detected by yolov5, and then a probability
distribution map is constructed according to the PROB algorithm. The feature
points extracted by one thread and the probability distribution map obtained
by the second thread will be transmitted as a topic respectively, and then the
pose estimation will be performed in the backend based on those feature points
and probability distribution map.

###  2.1 Scene Semantic Segmentation

In this paper, semantic segmentation is defined as identifying the content and
position of each object in the scene. This paper mainly focuses on water cups,
display screens, and other objects that remain stationary in space. These
objects are stationary, and their shapes contain more features, such as
sharper edges or corners. These objects contain more feature points, and
because these objects are stationary, the feature points on these objects are
more stable than other feature points. We believe that the feature points
contained in these stationary objects can maintain better stability after the
camera moves. Therefore, it is more reliable for localization and camera pose
estimation, and dynamic objects such as people tend to exacerbate the
uncertainty brought by semantic segmentation, so we directly regard dynamic
objects as part of the background. We assign higher confidence to the feature
points on these particular stationary objects to reduce the probability of
feature mismatch and the computational burden. We construct a set of static
objects that we focus on based on predefined knowledge and use yolov5
according to the set of static objects. Perform scene semantic segmentation in
this paper.

###  2.2 Construction of confidence distribution map

We construct a confidence distribution map based on the position of the center
point of the static object obtained by scene semantic segmentation. We try two
distributions, normal and Gaussian distributions, and compare the robustness
and accuracy of the two distributions to our method. Select Gaussian as the
distribution. Introducing a Gaussian distribution into the bounding box can
effectively alleviate the negative impact of inaccurate semantic segmentation.
The two-dimensional Gaussian distribution can minimize the impact of non-
target static objects within the bounding box on subsequent pose estimation.
Influences. We take the center of the stationary object obtained from the
scene semantic segmentation as the center of the two-dimensional Gaussian
distribution, assign the center of the two-dimensional Gaussian distribution
with a confidence of 0.99, and assign the edge of the two-dimensional Gaussian
distribution and the area outside the edge with a confidence of 0.1. The
calculation is the same as the size of the scene, and each pixel has a
confidence distribution map of its corresponding feature points; that is, a
confidence attribute ${prob}_{i,j}$ is assigned to each point $p_{i,j}$

${prob}_{i,j}=\left\\{\begin{array}[]{lr}\frac{1}{{2\pi}^{\frac{d}{2}}\varepsilon^{\frac{1}{2}}}\exp[-\frac{1}{2}\left(X-u)^{T}\varepsilon^{-1}\left(X-u\right)\right],ifintheboundingbox&\\\
&\\\ 0.1,ifouttheboundingbox&\end{array}\right.$ (1)

Among them, $\varepsilon$ is the covariance matrix describing the correlation
between variables, d is the variable dimension, u is the center coordinate
vector of the semantic segmentation box, and $X$=$(i,j)$ is the coordinate
vector of the point $p_{i,j}$ ,$i$and $j$ are the x and y-axis coordinates of
the pixel’s position in the screen, respectively, so ${prob}_{i,j}$ is
calculated according to the pixel’s position on the image. Suppose the same
pixel appears in multiple In the case of the bounding box, according to our
comparative experiments. In that case, we finally select the maximum value of
the probability values calculated in each bounding box as the probability
value of the point.

###  2.3 BA Problem Algorithm Based on Confidence Distribution Graph

In the SLAM process, Xiang Gao et al.[23] proposed that, it is usually
necessary to infer the pose and positioning according to the noise due to
various interference and misrecognition noise. According to the confidence
distribution map, we assign confidence to each pixel of each frame of the
picture. Attribute ${prob}_{i,j}$, when the feature point extraction is
completed, and we can optimize the back-end according to the confidence
attribute of the feature point. Since SLAM uses G2O optimization to solve the
Bundle Adjustment problem, we multiply the probability attribute into the
covariance matrix corresponding to each error term of the least squares
problem constructed by the Bundle Adjustment problem to introduce the
probability attribute to each error term. Weights are adjusted.

$T^{\ast}=\arg{min{\frac{1}{2}}}\sum_{k=1}^{n}{{prob}_{k}\ast||}U_{k}-\frac{1}{s_{k}}KTP_{k}||_{2}^{2}\,,$
(2)

$T$ is the Lie group of the camera pose $R$, $t$, $u$ is the projection
coordinate of a particular spatial point
$P_{k}={X_{k},Y_{k},Z_{k}}$,$U_{k}={u_{k},v_{k}}^{T}$, $K$ is the camera
internal parameter matrix, in the g2o optimizer In, we control the weight of
the edge to achieve the same purpose of controlling the weight of the error
term, to realize the prob-based BA problem-solving method.

---
Figure 2:  This picture shows the local working principle of the probability
map. The target detection network captures objects for identification and
defines the objects in the form of Gaussian distribution. The center position
is defined by the confidence of the target detection object. The probability
map gradually reduces the probability as the object’s center radiates to the
outer circle. The numbers in the picture are the weight of the approximate
position of the object.

##  3 Specific Implementation

###  3.1 Target detection optimizes

This experiment is based on the ROS system for data transmission. In our
scheme, on the one hand, a lightweight network, yolov5, is used for semantic
segmentation. On the other hand, the positioning function of the target
detection network is used to obtain information related to the target
recognition object. For example, the bounding box’s center coordinate
information and the bounding box’s side length information make target
detection a crucial auxiliary tool for SLAM positioning. To alleviate the
impact of the target detection network on the timeliness of trying SLAM as
much as possible, we choose YOLOv5 for target detection. We determine the set
of dynamic and static objects that may appear in the scene according to the
environment and prerequisites. Based on this target recognition. After
obtaining the recognition result, we perform semantic segmentation on the
original image and finally obtain the bounding box’s center position and side
length information.

###  3.2 Probability graph thread

Object detection techniques have great advantages in real-time performance
compared with purely semantic segmentation methods but cannot provide accurate
object masks. In the indoor dynamic SLAM scene, by setting up a probability
distribution map module after object detection, we reduce the influence of
these feature points in the subsequent process by assigning low confidence to
each feature point distributed in the non-target recognition object. At the
same time, according to the two-dimensional Gaussian distribution formula, the
confidence of the feature points in each bounding box is defined for the
bounding box information obtained in the semantic segmentation, and the
attention to the particular static objects existing in the environment is
strengthened. The distribution is distributed on these particular static
objects. The feature points are more involved in optimization and pose
estimation, which enhances positioning accuracy. At the same time, the two-
dimensional Gaussian distribution is used because it can reduce the negative
impact of inaccurate semantic segmentation as much as possible, thereby
improving the effect of camera pose estimation, improving the accuracy of
camera pose estimation, and reducing the amount of computation. Finally, we
construct a confidence matrix and transmit it as a new topic according to the
confidence prob attribute obtained from the two-dimensional Gaussian
distribution.

###  3.3 Optimization in G2O

After ORB-SLAM2 normally processes the lens image information from the
monocular camera, we combine the probability distribution map with the
original static SLAM and multiply the probability attributes of each
positioning point into the Bundle Adjustment problem constructed by the Bundle
Adjustment problem. The covariance matrix corresponds to each error term of
the good positive assist.

##  4 Experimental Evaluation

In this section, we use TUM’s RGB-D dataset[21], which has more dynamic
objects, to test the performance optimization of our proposed algorithm for
SLAM that introduces semantic segmentation methods. The metrics used to
evaluate the accuracy are absolute trajectory error (ATE) and relative pose
error (RPE). ATE stands for global consistency of trajectories. RPE includes
translation drift and rotation drift. The root means square error (RMSE) and
standard deviation (S.D.) of both are used to represent the robustness and
stability of the system[22].

We first show the impact of the PROB algorithm on the positioning points of
the original SLAM to detect whether the PROB algorithm will cause too much
performance impact on the original ORB-SLAM2[5] and then compare our method
with the original ORB-SLAM2 running map trajectory, To determine whether the
error has been reduced, to achieve the optimal effect, and then design a
series of ablation experiments to test the influence of the PROB module, as
well as the natural effect of the probability map concept created by running
it alone, and finally conduct real-time analysis and calculation. The size of
the impact on time efficiency. All experiments were performed on a computer
with an Intel i7 CPU, Nvidia 1070 GPU, and 16GB of memory.

---
Figure 3:  The level of feature points obtained in PROB-SLAM is consistent
with that of the original SLAM, and there is no reduction in the number of
feature points. Thus, the addition of target detection and the corresponding
probability map construction thread has little impact on SLAM performance.

###  4.1 Anchor point performance comparison experiment

In the ORB-SLAM operation scene, the motion of the object, the unclear object
to be detected in the image presented in the camera’s field of view, the
blurred image caused by the rotation of the camera, and the singular angle of
view all bring severe challenges to SLAM positioning, and it is easy to locate
errors. If it increases, it will even lead to the loss of positioning. The
following figure is a typical situation.

---
Figure 4:  The result is processed by the PROB algorithm. We selected several
locations where traditional SALM is prone to significant errors for effect
display. Due to the presence of dynamic objects and some unclear fields of
view, the original SLAM has significant errors. The experimental results show
that the PROB algorithm can effectively alleviate the error problem in these
scenarios, and the specific data will be shown later.
---
Figure 5:  There are dynamic objects moving fast in these scenes, which will
cause large errors in some existing slams that use semantic information. At
the same time, due to the problem of unclear vision in these scenes, some
errors will also be introduced. The PROB-SLAM algorithm in this paper can
effectively reduce some errors in these scenarios.

It is worth noting that there is no performance loss in the scene where many
objects are recognized for target detection, and the performance is lower than
the original version—many burdens and impacts. However, static SALM cannot
discriminate whether the character is moving, resulting in a misjudgment of
critical points’ dynamic and static attributes. We use the PROB algorithm to
optimize critical points based on the target detection probability map, which
can well avoid this problem. Through the processing of the PROB algorithm,
experiments have proved that the error, in this case, is significantly reduced
according to the data response of multiple evaluation indicators. The
experimental results fully demonstrate the effectiveness and robustness of the
PROB algorithm.

###  4.2 Running Error Comparison

Our experimental results with ORB-SLAM2 show that, unlike the PROB-SLAM
algorithm, which has advantages over ORB-SLAM2 in both dynamic and static
scenes, the algorithm can achieve a specific improvement in both dynamic and
static scenes. The APE and RPE graphs of our algorithm are as follows:

---
Figure 6:  In the line graph, raw represents ORB-SLAM2, and PROB-SLAM
represents the system. The two sets of lines correspond to the absolute errors
when running the same data set. It can be seen from the figure that when ORB-
SLAM2 is at the highest point of error, PROB-SLAM is relatively less than
about 15 percent of its error.

This line chart shows the comparison between the original ORB-SLAM2 and the
modified version, where POSE is the version with the probability map added,
and POSE-DYNAMIC uses YOLO to remove dynamic objects for predefine knowledge,
which shows that in the range of error, It will be improved compared to
traditional visual SLAM.

---
Figure 7:  It is the relative error of the two sets of data. It can be seen
from the figure that the relative error distribution position of PROB-SLAM is
also relatively lower than that of ORB-SLAM2, and the maximum relative error
distribution is about 30% smaller than that of ORB-SLAM2.

###  4.3 Separate module experiments

To prove the function of our PROB algorithm module, we designed a slice
experiment, among them, PROB-SLAM: the algorithm of this paper Only-PROB:
direct target detection and convert the target detection image information
into a probability map form.

he results show that the PROB algorithm can directly improve the confidence of
the position of crucial points for static objects and can successfully adjust
the influence of dynamic objects. It can play a perfect optimization effect in
the process of object movement. If Only-PROB adjusts the contrast adjustment
index of the probability map too large, it will also cause the loss of
positioning.

###  4.4 Processing time comparison

Real-time performance is a significant indicator of the SLAM system, and a
more considerable delay will cause a more significant error in SLAM
positioning. We tested the average running time of each module, as shown in
the table. It includes the time required for ORB-SLAM2 original processing,
the time required for PROB-SLAM processing, and the time required for image
processing by a separate PROB algorithm. YOLOv5-based semantic threading runs
in parallel with ORB feature extraction. The results show that the average
processing time per frame of the PROB-SLAM main thread is:

Table 1: Processing time of each module. Methods | YOLO | PROB | Tracking
---|---|---|---
ORB-SLAM | / | / | 36.5 ms
PROB-SLAM | 12.28 ms | 0.12 ms | 41.27 ms

##  5 Conclusion

This paper proposes an independent thread for constructing a probability
distribution map based on semantic information, which can reduce the impact of
semantic information’s uncertainty. Because it is an independent thread, it
can be easily integrated into various existing SLAMs. In the system, it will
not affect the performance of the original system, so it has a wide range of
application scenarios. For example, in some industrial scenarios with fixed
particular objects, we can introduce this thread into the existing SLAM
system. Accuracy is improved by better utilization of semantic detection
information.

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