Long-term Map Management in Degraded Scenarios Based on Pose Reference and Information Density

In open-pit mines, long-term map management is crucial for route-planning of autonomous mining trucks during haulage. However, degraded scenarios with low-texture mountains may cause mapping discontinuity after rasterization. Moreover, the high-frequency environmental changes may lead to inter-session matching failures, resulting in significant mapping inaccuracies. To address the above issues, we propose a pose reference and information density based long-term mapping method for degraded scenarios. We introduce adaptive information compensation to ensure the even distribution of map information after the mapping process. Sequentially, we utilize poses corresponding to central and query sessions along with the information density model to achieve inter-session matching. Outdated information pruning is then employed to update the map. Furthermore, we evaluate the map quality assessing overlap and utilization rates under field tests, and the results suggest that our proposed methods yield over an 8% improvement in overlap rate compared to state-of-the-art methods.