A General Framework for Lifelong Localization and Mapping in Changing Environment
This addresses the need for long-term robot operation in dynamic settings, but it is incremental as it builds on existing SLAM methods with specific improvements.
The paper tackles the problem of maintaining an up-to-date map for robots in changing environments like supermarkets, presenting a lifelong SLAM framework that uses efficient map updating and trimming to manage memory, validated by over a month of real-world deployment.
The environment of most real-world scenarios such as malls and supermarkets changes at all times. A pre-built map that does not account for these changes becomes out-of-date easily. Therefore, it is necessary to have an up-to-date model of the environment to facilitate long-term operation of a robot. To this end, this paper presents a general lifelong simultaneous localization and mapping (SLAM) framework. Our framework uses a multiple session map representation, and exploits an efficient map updating strategy that includes map building, pose graph refinement and sparsification. To mitigate the unbounded increase of memory usage, we propose a map-trimming method based on the Chow-Liu maximum-mutual-information spanning tree. The proposed SLAM framework has been comprehensively validated by over a month of robot deployment in real supermarket environment. Furthermore, we release the dataset collected from the indoor and outdoor changing environment with the hope to accelerate lifelong SLAM research in the community. Our dataset is available at https://github.com/sanduan168/lifelong-SLAM-dataset.