Hierarchical Segment-based Optimization for SLAM

TL;DR

This paper introduces a hierarchical segment-based optimization approach for SLAM that enhances efficiency and robustness by segmenting trajectories, using a buffer mechanism, and hierarchically allocating computational resources based on frame errors.

Contribution

The paper proposes a novel hierarchical segmentation and buffer mechanism for SLAM optimization, improving efficiency and robustness over existing methods.

Findings

Significantly improves optimization efficiency

Maintains accuracy with minimal loss

Outperforms existing high-efficiency methods

Abstract

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.