WHU-PCPR: A cross-platform heterogeneous point cloud dataset for place recognition in complex urban scenes

TL;DR

WHU-PCPR is a new diverse, large-scale point cloud dataset from multiple platforms and sensors, designed to advance place recognition in complex urban environments with real-world variations.

Contribution

The paper introduces WHU-PCPR, a novel cross-platform heterogeneous point cloud dataset with extensive urban scenes, sensor diversity, and long-term coverage for improved PCPR research.

Findings

Extensive evaluation of PCPR methods on the dataset.

Identification of key challenges in cross-platform PCPR.

Insights into future research directions.

Abstract

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.