  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.
