Place recognition is crucial for robotic localization and loop closure in simultaneous localization and mapping (SLAM). Recently, LiDARs have gained popularity due to their robust sensing capability and measurement consistency, even in the illumination-variant environment, offering an advantage over traditional imaging sensors. Spinning LiDARs are widely accepted among many types, while non-repetitive scanning patterns have recently been utilized in robotic applications. Beyond the range measurements, some LiDARs offer additional measurements, such as reflectivity, Near Infrared (NIR), and velocity (e.g., FMCW LiDARs). Despite these advancements, a noticeable dearth of datasets comprehensively reflects the broad spectrum of LiDAR configurations optimized for place recognition. To tackle this issue, our paper proposes the HeLiPR dataset, curated especially for place recognition with heterogeneous LiDAR systems, embodying spatial-temporal variations. To the best of our knowledge, the HeLiPR dataset is the first heterogeneous LiDAR dataset designed to support inter-LiDAR place recognition with both non-repetitive and spinning LiDARs, accommodating different field of view (FOV) and varying numbers of rays. Encompassing the distinct LiDAR configurations, it captures varied environments ranging from urban cityscapes to high-dynamic freeways over a month, designed to enhance the adaptability and robustness of place recognition across diverse scenarios. Notably, the HeLiPR dataset also includes trajectories that parallel sequences from MulRan, underscoring its utility for research in heterogeneous LiDAR place recognition and long-term studies. The dataset is accessible at https: //sites.google.com/view/heliprdataset.

翻译：地点识别对于机器人定位及同步定位与地图构建（SLAM）中的闭环检测至关重要。近年来，激光雷达因其在光照变化环境下仍具备稳健感知能力和测量一致性而广受欢迎，相较于传统成像传感器具有优势。在众多激光雷达类型中，旋转式激光雷达被广泛采用，而非重复扫描模式近期也被应用于机器人领域。除距离测量外，部分激光雷达还提供反射率、近红外（NIR）和速度等附加测量值（例如FMCW激光雷达）。尽管取得了这些进展，但目前仍缺乏能够全面反映为地点识别优化的各类激光雷达配置的数据集。为解决这一问题，本文提出HeLiPR数据集，该数据集专为异构激光雷达系统下的地点识别而精心构建，并体现了时空变化特征。据我们所知，HeLiPR是首个支持非重复扫描与旋转式激光雷达间地点识别的异构激光雷达数据集，可适配不同视场角（FOV）和不同射线数量的激光雷达配置。该数据集覆盖了从城市街景到高动态高速公路的多样化环境，采集时长超过一个月，旨在提升地点识别在多种场景下的适应性和鲁棒性。值得注意的是，HeLiPR数据集还包含与MulRan序列轨迹平行的数据，突显了其在异构激光雷达地点识别及长期研究中的实用价值。数据集访问地址：https://sites.google.com/view/heliprdataset。