Due to the robustness in sensing, radar has been highlighted, overcoming harsh weather conditions such as fog and heavy snow. In this paper, we present a novel radar-only place recognition that measures the similarity score by utilizing Radon-transformed sinogram images and cross-correlation in frequency domain. Doing so achieves rigid transform invariance during place recognition, while ignoring the effects of radar multipath and ring noises. In addition, we compute the radar similarity distance using mutable threshold to mitigate variability of the similarity score, and reduce the time complexity of processing a copious radar data with hierarchical retrieval. We demonstrate the matching performance for both intra-session loop-closure detection and global place recognition using a publicly available imaging radar datasets. We verify reliable performance compared to existing stable radar place recognition method. Furthermore, codes for the proposed imaging radar place recognition is released for community.

翻译：摘要：得益于雷达在感知中的鲁棒性，其在雾天和暴雪等恶劣天气条件下展现出显著优势。本文提出一种纯雷达地点识别方法，通过利用Radon变换后的正弦图图像与频域互相关计算相似度分数，在实现刚性变换不变性的同时抑制雷达多路径效应和环形噪声的影响。此外，我们采用可变阈值计算雷达相似性距离以缓解相似度分数的波动性，并通过分层检索降低海量雷达数据的处理时间复杂度。基于公开成像雷达数据集，我们验证了该方法在会话内闭环检测与全局地点识别中的匹配性能。与现有稳定的雷达地点识别方法相比，本方法展现了更可靠的性能。同时，所提出的成像雷达地点识别代码已开源供社区使用。