Point Cloud-based Place Recognition (PCPR) demonstrates considerable potential in applications such as autonomous driving, robot localization and navigation, and map update. In practical applications, point clouds used for place recognition are often acquired from different platforms and LiDARs across varying scene. However, existing PCPR datasets lack diversity in scenes, platforms, and sensors, which limits the effective development of related research. To address this gap, we establish WHU-PCPR, a cross-platform heterogeneous point cloud dataset designed for place recognition. The dataset differentiates itself from existing datasets through its distinctive characteristics: 1) cross-platform heterogeneous point clouds: collected from survey-grade vehicle-mounted Mobile Laser Scanning (MLS) systems and low-cost Portable helmet-mounted Laser Scanning (PLS) systems, each equipped with distinct mechanical and solid-state LiDAR sensors. 2) Complex localization scenes: encompassing real-time and long-term changes in both urban and campus road scenes. 3) Large-scale spatial coverage: featuring 82.3 km of trajectory over a 60-month period and an unrepeated route of approximately 30 km. Based on WHU-PCPR, we conduct extensive evaluation and in-depth analysis of several representative PCPR methods, and provide a concise discussion of key challenges and future research directions. The dataset and benchmark code are available at https://github.com/zouxianghong/WHU-PCPR.

翻译：基于点云的地点识别在自动驾驶、机器人定位与导航以及地图更新等应用中展现出巨大潜力。在实际应用中，用于地点识别的点云通常采集自不同场景下的不同平台与激光雷达。然而，现有的点云地点识别数据集在场景、平台和传感器方面缺乏多样性，限制了相关研究的有效开展。为填补这一空白，我们构建了WHU-PCPR，一个专为地点识别设计的跨平台异构点云数据集。该数据集通过以下鲜明特征区别于现有数据集：1) 跨平台异构点云：采集自测量级车载移动激光扫描系统与低成本便携式头盔激光扫描系统，各系统配备不同的机械式与固态激光雷达传感器。2) 复杂的定位场景：涵盖城市与校园道路场景中的实时变化与长期变化。3) 大规模空间覆盖：包含60个月内总长82.3公里的轨迹，以及一条约30公里的非重复路线。基于WHU-PCPR，我们对多种代表性点云地点识别方法进行了广泛评估与深入分析，并对关键挑战与未来研究方向进行了简要讨论。数据集与基准代码公开于 https://github.com/zouxianghong/WHU-PCPR。