Abstract
Visual simultaneous localization and mapping (SLAM) is well-known to be one of the research areas in robotics. There are many challenges in traditional point feature-based approaches, such as insufficient point features, motion jitter, and low localization accuracy in low-texture scenes, which reduce the performance of the algorithms. In this article, we propose an RGB-D SLAM system to handle these situations, which is named Point-Line Fusion (PLF)-SLAM. We utilize both points and line segments throughout the process of our work. Specifically, we present a new line segment extraction method to solve the overlap or branch problem of the line segments, and then a more rigorous screening mechanism is proposed in the line matching section. Instead of minimizing the reprojection error of points, we introduce the reprojection error based on points and lines to get a more accurate tracking pose. In addition, we come up with a solution to handle the jitter frame, which greatly improves tracking success rate and availability of the system. We thoroughly evaluate our system on the Technische Universität München (TUM) RGB-D benchmark and compare it with ORB-SLAM2, presumably the current state-of-the-art solution. The experiments show that our system has better accuracy and robustness compared to the ORB-SLAM2.
Highlights
Simultaneous localization and mapping (SLAM) is an extensively researched topic in robotics[1] and has been widely applied in service robot, autonomous driving, Unmanned Aerial Vehicle (UAV), virtual reality, and other fields.[2,3,4] The SLAM system mainly contains laser-based and vision-based methods according to its sensor type
Visual information has the advantage of much quantity information, low cost, and intuitive effects, visual simultaneous localization and mapping has gradually become the hot research field.[7]
It achieves the reconstruction of semi-dense scene on standard Central Processing Unit (CPU)s, without Graphic Processing Unit (GPU) acceleration, and guarantees the final stability and real-time, but it still needs to rely on the feature point method for loop detection
Summary
Simultaneous localization and mapping (SLAM) is an extensively researched topic in robotics[1] and has been widely applied in service robot, autonomous driving, Unmanned Aerial Vehicle (UAV), virtual reality, and other fields.[2,3,4] The SLAM system mainly contains laser-based and vision-based methods according to its sensor type. Visual odometry is the process of determining the position and orientation of a robot by analyzing the associated camera images It has been used in a wide variety of robotic applications. (LSD)-SLAM proposed by Engel et al.,[8] which is able to build large-scale semi-dense maps, using direct methods instead of bundle adjustment over features. It achieves the reconstruction of semi-dense scene on standard Central Processing Unit (CPU)s, without Graphic Processing Unit (GPU) acceleration, and guarantees the final stability and real-time, but it still needs to rely on the feature point method for loop detection. The second section discusses the related work, the third section gives the details of our proposal, the fourth section details the experimental results, and the fifth section presents the conclusions and the future work
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