用雷达实现抗恶劣天气的轻量级定位，不依赖可见光传感器。
ReFeree: Radar-Based Lightweight and Robust Localization using Feature and Free space
- 采用一维环形描述子，实现旋转不变与轻量化。
- 利用自由空间与特征点噪声相反特性，提升抗误检能力。
- 适合嵌入式设备，可辅助构建低算力下的SLAM系统。
场景识别在实现长期自主性中至关重要。真实世界中的机器人面临多种天气条件（如阴天、大雨、下雪），而大多数传感器（如摄像头、激光雷达）工作于或近可见光波段，对恶劣天气敏感，导致定位困难。相比之下，雷达因使用长波电磁波，受环境变化影响小，具备天气独立性，正受到关注。本文提出一种基于雷达的轻量级、鲁棒的场景识别方法。通过选取一维环形描述子实现旋转不变性与轻量化，并利用自由空间与特征点噪声特性相反的特点，缓解误检影响。此外，可估计初始朝向，有助于构建融合里程计与注册的SLAM流程，兼顾车载计算资源。所提方法在多种场景下进行严格验证（包括单次会话、多次会话及不同天气条件）。特别地，在缺乏结构信息的极端环境（如OORD数据集）中，仍实现了可靠的场景识别性能。
原文摘要 · Abstract (English)
Place recognition plays an important role in achieving robust long-term autonomy. Real-world robots face a wide range of weather conditions (e.g. overcast, heavy rain, and snowing) and most sensors (i.e. camera, LiDAR) essentially functioning within or near-visible electromagnetic waves are sensitive to adverse weather conditions, making reliable localization difficult. In contrast, radar is gaining traction due to long electromagnetic waves, which are less affected by environmental changes and weather independence. In this work, we propose a radar-based lightweight and robust place recognition. We achieve rotational invariance and lightweight by selecting a one-dimensional ring-shaped description and robustness by mitigating the impact of false detection utilizing opposite noise characteristics between free space and feature. In addition, the initial heading can be estimated, which can assist in building a SLAM pipeline that combines odometry and registration, which takes into account onboard computing. The proposed method was tested for rigorous validation across various scenarios (i.e. single session, multi-session, and different weather conditions). In particular, we validate our descriptor achieving reliable place recognition performance through the results of extreme environments that lacked structural information such as an OORD dataset.
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