Papers › ForestLPR: LiDAR Place Recognition in Forests Attentioning Multiple BEV Density Images
ForestLPR: LiDAR Place Recognition in Forests Attentioning Multiple BEV Density Images
Yanqing Shen, Turcan Tuna, Marco Hutter, Cesar Cadena, Nanning Zheng
Place recognition is essential to maintain global consistency in large-scale localization systems. While research in urban environments has progressed significantly using LiDARs or cameras, applications in natural forest-like environments remain largely under-explored. Furthermore, forests present particular challenges due to high self-similarity and substantial variations in vegetation growth over time. In this work, we propose a robust LiDAR-based place recognition method for natural forests, ForestLPR. We hypothesize that a set of cross-sectional images of the forest's geometry at different heights contains the information needed to recognize revisiting a place. The cross-sectional images are represented by \ac{bev} density images of horizontal slices of the point cloud at different heights. Our approach utilizes a visual transformer as the shared backbone to produce sets of local descriptors and introduces a multi-BEV interaction module to attend to information at different heights adaptively. It is followed by an aggregation layer that produces a rotation-invariant place descriptor. We evaluated the efficacy of our method extensively on real-world data from public benchmarks as well as robotic datasets and compared it against the state-of-the-art (SOTA) methods. The results indicate that ForestLPR has consistently good performance on all evaluations and achieves an average increase of 7.38\% and 9.11\% on Recall@1 over the closest competitor on intra-sequence loop closure detection and inter-sequence re-localization, respectively, validating our hypothesis
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 3D Place Recognition | Wild-Places | ForestLPR | AR@1 (Intra-Seq) | 77.62 | #1 of 4 | Archive leaderboard | report |
| 3D Place Recognition | Wild-Places | ForestLPR | AR@1 Inter-Seq | 78.73 | #1 of 4 | Archive leaderboard | report |
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Methods
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