Abstract
The scene rigidity is a strong assumption in typical visual Simultaneous Localization and Mapping (vSLAM) algorithms. Such strong assumption limits the usage of most vSLAM in dynamic real-world environments, which are the target of several relevant applications such as augmented reality, semantic mapping, unmanned autonomous vehicles, and service robotics. Many solutions are proposed that use different kinds of semantic segmentation methods (e.g., Mask R-CNN, SegNet) to detect dynamic objects and remove outliers. However, as far as we know, such kind of methods wait for the semantic results in the tracking thread in their architecture, and the processing time depends on the segmentation methods used. In this paper, we present RDS-SLAM, a real-time visual dynamic SLAM algorithm that is built on ORB-SLAM3 and adds a semantic thread and a semantic-based optimization thread for robust tracking and mapping in dynamic environments in real-time. These novel threads run in parallel with the others, and therefore the tracking thread does not need to wait for the semantic information anymore. Besides, we propose an algorithm to obtain as the latest semantic information as possible, thereby making it possible to use segmentation methods with different speeds in a uniform way. We update and propagate semantic information using the moving probability, which is saved in the map and used to remove outliers from tracking using a data association algorithm. Finally, we evaluate the tracking accuracy and real-time performance using the public TUM RGB-D datasets and Kinect camera in dynamic indoor scenarios. Source code and demo: https://github.com/yubaoliu/RDS-SLAM.git.
Highlights
Simultaneous localization and mapping (SLAM) [1] is a fundamental technique for many applications such as augmented reality (AR), robotics, and unmanned autonomous vehicles (UAV)
The main contributions of this paper are: (1) we propose a novel semantic-based real-time dynamic Visual SLAM (vSLAM) algorithm, RDS-SLAM, which enables the tracking thread does not need to wait for the semantic results anymore
This method efficiently and effectively uses semantic segmentation results for dynamic object detection and outliers removing while keeping the algorithm’s real-time nature. (2) we propose a keyframe selection strategy that uses as the latest new semantic information as possible for outliers removal with any semantic segmentation methods with different speeds in a uniform way
Summary
Simultaneous localization and mapping (SLAM) [1] is a fundamental technique for many applications such as augmented reality (AR), robotics, and unmanned autonomous vehicles (UAV). Mur-Artal et al presented ORB-SLAM2 [16], a complete SLAM system for monocular, stereo, and RGB-D cameras, which works in real-time on standard CPUs in a wide variety of environments This system estimates the ego-motion of the camera by matching the corresponding ORB [17] features between the current frame and previous frames and has three parallel threads: tracking, local mapping, and loop closing. Fan et al [8] proposed a novel semantic SLAM system with a more accurate point cloud map in dynamic environments and they use BlizNet [27] to obtain the masks and bounding boxes of the dynamic objects in the image All these methods use the blocked model. We skip the detailed explanations of the modules that are the same as those of ORB-SLAM3
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