RaPlace：基于Radon变换与可变阈值的成像雷达地点识别
RaPlace: Place Recognition for Imaging Radar using Radon Transform and Mutable Threshold
AI总结：
本文提出RaPlace，利用Radon变换正弦图和频域互相关实现刚体变换不变的雷达地点识别，并通过可变阈值与分层检索提升鲁棒性和效率。
摘要：
由于雷达在感知方面的鲁棒性，它能够克服雾、大雪等恶劣天气条件，因而受到关注。本文提出一种新颖的仅使用雷达的地点识别方法，该方法利用Radon变换后的正弦图图像和频域互相关来度量相似度分数。这样做能够在地点识别过程中实现刚体变换不变性，同时忽略雷达多径效应和环状噪声的影响。此外，我们使用可变阈值计算雷达相似度距离，以缓解相似度分数的变异性，并通过分层检索降低处理大量雷达数据的时间复杂度。我们利用公开可用的成像雷达数据集，展示了在会话内回环检测和全局地点识别中的匹配性能。我们验证了与现有稳定的雷达地点识别方法相比具有可靠的性能。此外，所提出的成像雷达地点识别代码已向社区发布。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
