A Long-Term SLAM System With Graph-Structured Maps and Online Maintenance Using Dual-Layer Octree Change Detection

Environmental changes over extended periods pose significant challenges for map-based navigation applications, such as map volume expansion, localization errors caused by environmental changes or incomplete map coverage, and the inability to update maps in real time. This article introduces a novel long-term light detection and ranging (LiDAR) simultaneous localization and mapping (SLAM) system that organizes maps into a graph structure, combines map-based localization with odometry data through pose fusion, and implements an efficient real-time change detection and map update mechanism, ensuring continuous positioning and map maintenance. To maintain manageable map volumes and simplify usage, key frames from laser scans are aggregated into submaps, connected via traversable paths within the graph. The system temporarily relies on odometry for short-term localization, reoptimizing when map conditions improve to achieve more accurate and robust positioning. A dual-layer octree with a ray-casting method is also integrated for rapid real-time change detection and map updates. Extensive testing on the NCLT long-term dataset and our self-collected factory dataset demonstrates that the proposed algorithm effectively controls map size, enhances localization accuracy and robustness, and supports real-time map updates.