A lifelong mapping framework with multi-view projection in a changing environment

Simultaneous Localization and Mapping (SLAM) systems typically rely on a prior map constructed during an initial deployment. In real-world environments, however, structural and semantic changes gradually invalidate this map, leading to degraded localization accuracy and, in severe cases, localization failure. This limitation poses a major obstacle to the long-term deployment of mobile robots in dynamic environments. This paper proposes a lifelong mapping framework with multi-view projection fusion (LLMF) that enables efficient map maintenance while preserving a consistent global coordinate system. The framework introduces two key design components. First, a multi-view point cloud projection alignment strategy based on Bird’s-Eye View (BEV) and frontal view (FV) projections is employed to align point cloud maps acquired at different times without re-labeling previously defined operational points. Second, an image-based change detection and map update mechanism is developed, transforming computationally expensive 3D point cloud comparisons into efficient 2D image processing operations. The proposed framework is evaluated through qualitative experiments on the open-source MulRan dataset and quantitative long-term experiments conducted over more than nine months in a real farm environment. Experimental results demonstrate that LLMF maintains localization accuracy while significantly reducing the computational cost of change detection, lowering processing time from several hours to a few minutes. These results indicate that the proposed framework provides a practical and scalable engineering solution for long-term robot operation in changing environments.