MS‐SLAM: Memory‐Efficient Visual SLAM With Sliding Window Map Sparsification

While most visual SLAM systems traditionally prioritize accuracy or speed, the associated memory consumption would also become a concern for robots working in large‐scale environments, primarily due to the perpetual preservation of increasing number of redundant map points. Although these redundant map points are initially constructed to ensure robust frame tracking, they contribute little once the robot moves to other locations and are primarily kept for potential loop closure. After continuous optimization, these map points are accurate and actually not all of them are essential for loop closure. Therefore, this paper proposes MS‐SLAM, a memory‐efficient visual SLAM system with map sparsification aimed at selecting only parts of useful map points to keep in the global map. In MS‐SLAM, all local map points are temporarily kept to ensure robust frame tracking and further optimization, while redundant nonlocal map points are removed through the proposed novel sliding window map sparsification, which is efficient and running concurrently with original SLAM tracking. The loop closure still operates well with the selected useful map points. Through exhaustive experiments across various scenes in both public and self‐collected data sets, MS‐SLAM has demonstrated comparable accuracy with the state‐of‐the‐art visual SLAM while significantly reducing memory consumption by over 70% in large‐scale scenes. This facilitates the scalability of visual SLAM in large‐scale environments, making it a promising solution for real‐world applications. We will release our codes at https://github.com/fishmarch/MS-SLAM.