A life-long SLAM approach using adaptable local maps based on rasterized LIDAR images

TL;DR

This paper introduces a life-long SLAM system that uses rasterized local maps and visual words for loop closure, achieving high accuracy with lower computational costs in dynamic environments.

Contribution

It proposes a novel rasterized image-based map representation combined with a visual words loop closure method for efficient long-term SLAM.

Findings

Loop closure recall and precision above 90%

Lower computational cost compared to state-of-the-art methods

Effective in both outdoor and indoor datasets

Abstract

Most real-time autonomous robot applications require a robot to traverse through a dynamic space for a long time. In some cases, a robot needs to work in the same environment. Such applications give rise to the problem of a life-long SLAM system. Life-long SLAM presents two main challenges i.e. the tracking should not fail in a dynamic environment and the need for a robust and efficient mapping strategy. The system should update maps with new information; while also keeping track of older observations. But, mapping for a long time can require higher computational requirements. In this paper, we propose a solution to the problem of life-long SLAM. We represent the global map as a set of rasterized images of local maps along with a map management system responsible for updating local maps and keeping track of older values. We also present an efficient approach of using the bag of visual words method for loop closure detection and relocalization. We evaluate the performance of our system on the KITTI dataset and an indoor dataset. Our loop closure system reported recall and precision of above 90 percent. The computational cost of our system is much lower as compared to state-of-the-art methods. Our method reports lower computational requirements even for long-term operation.