A Life-Long SLAM Approach Using Adaptable Local Maps Based on Rasterized LIDAR Images

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.

Automated summary

The paper proposes a solution to the problem of life-long SLAM, where the global map is represented as a set of rasterized images of local maps with a map management system updating local maps and tracking older values. Utilizing the bag of visual words method for loop closure detection and relocalization, the system performs well on the KITTI dataset and an indoor dataset with stable performance and low computational cost.