Online Robust Sliding-Windowed LiDAR SLAM in Natural Environments
This addresses SLAM for autonomous environmental monitoring in natural habitats, but appears incremental as it combines existing techniques like robust weighting and sliding-windowed optimization.
The paper tackles the problem of SLAM in natural environments by presenting an online graph-based SLAM system for 2D LiDAR, achieving stable performance in cluttered surroundings while meeting real-time constraints.
Despite the growing interest for autonomous environmental monitoring, effective SLAM realization in native habitats remains largely unsolved. In this paper, we fill this gap by presenting a novel online graph-based SLAM system for 2D LiDAR sensor in natural environments. By taking advantage of robust weighting scheme, sliding-windowed optimization, fast scan-matcher and parallel computing, our system not only delivers stable performance in cluttered surroudings but also meets real-time constraint. Simulated and experimental results confirm the feasibility and efficiency in the overall design of the proposed system.