Submap-based Pose-graph Visual SLAM: A Robust Visual Exploration and Localization System
This work addresses robustness issues in VSLAM for applications like robotics and autonomous navigation, though it appears incremental as it builds on existing submap and pose-graph concepts.
The paper tackles the challenge of robust visual localization in VSLAM under difficult conditions like textureless frames and motion blur by proposing a submap-based system, resulting in improved tracking percentage and reduced ATE RMSE compared to state-of-the-art methods.
For VSLAM (Visual Simultaneous Localization and Mapping), localization is a challenging task, especially for some challenging situations: textureless frames, motion blur, etc.. To build a robust exploration and localization system in a given space or environment, a submap-based VSLAM system is proposed in this paper. Our system uses a submap back-end and a visual front-end. The main advantage of our system is its robustness with respect to tracking failure, a common problem in current VSLAM algorithms. The robustness of our system is compared with the state-of-the-art in terms of average tracking percentage. The precision of our system is also evaluated in terms of ATE (absolute trajectory error) RMSE (root mean square error) comparing the state-of-the-art. The ability of our system in solving the `kidnapped' problem is demonstrated. Our system can improve the robustness of visual localization in challenging situations.