Place recognition, an essential challenge in computer vision and robotics, involves identifying previously visited locations. Despite algorithmic progress, challenges related to appearance change persist, with existing datasets often focusing on seasonal and weather variations but overlooking terrain changes. Understanding terrain alterations becomes critical for effective place recognition, given the aging infrastructure and ongoing city repairs. For real-world applicability, the comprehensive evaluation of algorithms must consider spatial dynamics. To address existing limitations, we present a novel multi-session place recognition dataset acquired from an active construction site. Our dataset captures ongoing construction progress through multiple data collections, facilitating evaluation in dynamic environments. It includes camera images, LiDAR point cloud data, and IMU data, enabling visual and LiDAR-based place recognition techniques, and supporting sensor fusion. Additionally, we provide ground truth information for range-based place recognition evaluation. Our dataset aims to advance place recognition algorithms in challenging and dynamic settings. Our dataset is available at https://github.com/dongjae0107/ConPR.

翻译：地点识别是计算机视觉与机器人学中的一个核心挑战，涉及对先前访问过的位置进行识别。尽管算法已取得进展，但与外观变化相关的难题依然存在，现有数据集通常关注季节与天气变化，却忽略了地形改变。考虑到基础设施的老化与城市持续维修的现实，理解地形变化对于实现有效的地点识别至关重要。为提升实际应用价值，算法的全面评估必须考虑空间动态性。为弥补现有不足，我们提出了一个从活跃建筑工地采集的新型多时段地点识别数据集。本数据集通过多次数据采集记录了施工的持续进展，便于在动态环境中进行评估。它包含相机图像、LiDAR点云数据与IMU数据，支持基于视觉和LiDAR的地点识别技术，并可用于传感器融合研究。此外，我们还提供了用于基于距离的地点识别评估的真实轨迹信息。本数据集旨在推动地点识别算法在具有挑战性的动态场景中的发展。数据集发布于 https://github.com/dongjae0107/ConPR。