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

2025-03-06

AI Summary

To address the challenges of high self-similarity and vegetation-induced temporal variability in LiDAR-based place recognition within natural forests, this paper proposes a multi-height bird’s-eye-view (BEV) density representation coupled with rotation-invariant descriptor learning. Methodologically, we first construct multi-layer height-sliced BEV density images to explicitly encode vertical geometric structure; second, we design a learnable cross-height adaptive attention module to fuse multi-scale spatial features; third, we introduce a rotation-equivariant feature aggregation mechanism to generate robust global descriptors. Evaluated on public benchmarks and real-world robot-collected datasets, our approach achieves state-of-the-art performance: it improves same-sequence loop closure recall@1 by 7.38% and cross-sequence relocalization accuracy by 9.11% over prior methods.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Multi-modal Vision
• Machine Learning: Multi-instance/Multi-view Learning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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

Problem

• Develops LiDAR-based place recognition for natural forests.
• Addresses high self-similarity and vegetation growth variations.
• Improves recall rates in loop closure and re-localization.

Innovation

• LiDAR-based place recognition in forests
• Multiple BEV density images for recognition
• Visual transformer with multi-BEV interaction