ForestLPR: LiDAR Place Recognition in Forests Attentioning Multiple BEV Density Images

• URL: http://arxiv.org/abs/2503.04475v1
• Date: Thu, 06 Mar 2025 14:24:22 GMT
• Title: ForestLPR: LiDAR Place Recognition in Forests Attentioning Multiple BEV Density Images
• Authors: Yanqing Shen, Turcan Tuna, Marco Hutter, Cesar Cadena, Nanning Zheng,
• Abstract summary: We propose a robust LiDAR-based place recognition method for natural forests, ForestLPR.<n>Cross-sectional images of the forest's geometry at different heights contain the information needed to recognize revisiting a place.<n>Our approach utilizes a visual transformer as the shared backbone to produce sets of local descriptors.
• Score: 38.727720300337296
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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