无需训练的激光雷达定位方法，跨城市与自然环境实现高精度定位
Matched Filtering based LiDAR Place Recognition for Urban and Natural Environments
- 基于鸟瞰图描述符和匹配滤波的两阶段搜索策略
- 在多个数据集上召回率比当前最优提升15%
- 适合无标注数据或实时部署的复杂环境导航
位置识别是自主导航中的关键任务，旨在从初始遍历中重新识别已访问的位置。与视觉位置识别（VPR）不同，激光雷达位置识别（LPR）对光照、季节和纹理变化具有鲁棒性，在结构化城市环境基准数据集上表现优异。然而，当前对能在多样环境中保持高性能且无需大量训练的方法需求日益增长。本文提出一种手工设计的匹配策略，可实现城市与非结构化自然环境下的旋转变换不变位置识别及相对位姿估计。该方法构建鸟瞰图（BEV）全局描述符，并采用两阶段匹配滤波搜索——一种用于在噪声中检测已知信号的信号处理技术。在NCLT、Oxford Radar和WildPlaces数据集上的广泛测试表明，该方法在位置识别与相对位姿估计指标上均达到当前最优（SoTA）性能，召回率最高比前人最优结果高出15%。
原文摘要 · Abstract (English)
Place recognition is an important task within autonomous navigation, involving the re-identification of previously visited locations from an initial traverse. Unlike visual place recognition (VPR), LiDAR place recognition (LPR) is tolerant to changes in lighting, seasons, and textures, leading to high performance on benchmark datasets from structured urban environments. However, there is a growing need for methods that can operate in diverse environments with high performance and minimal training. In this paper, we propose a handcrafted matching strategy that performs roto-translation invariant place recognition and relative pose estimation for both urban and unstructured natural environments. Our approach constructs Birds Eye View (BEV) global descriptors and employs a two-stage search using matched filtering -- a signal processing technique for detecting known signals amidst noise. Extensive testing on the NCLT, Oxford Radar, and WildPlaces datasets consistently demonstrates state-of-the-art (SoTA) performance across place recognition and relative pose estimation metrics, with up to 15% higher recall than previous SoTA.
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