Hierarchical Segment-based Optimization for SLAM
This addresses computational bottlenecks in SLAM systems for robotics and autonomous navigation, though it appears incremental as it builds on existing optimization frameworks.
The paper tackles the computational efficiency problem in SLAM back-end optimization by proposing a hierarchical segment-based method that allocates computation according to frame error, achieving greatly improved efficiency with almost no accuracy drop and outperforming existing high-efficiency methods.
This paper presents a hierarchical segment-based optimization method for Simultaneous Localization and Mapping (SLAM) system. First we propose a reliable trajectory segmentation method that can be used to increase efficiency in the back-end optimization. Then we propose a buffer mechanism for the first time to improve the robustness of the segmentation. During the optimization, we use global information to optimize the frames with large error, and interpolation instead of optimization to update well-estimated frames to hierarchically allocate the amount of computation according to error of each frame. Comparative experiments on the benchmark show that our method greatly improves the efficiency of optimization with almost no drop in accuracy, and outperforms existing high-efficiency optimization method by a large margin.