JORD: A Benchmark Dataset for Off-Road LiDAR Place Recognition and SLAM

Simultaneous localization and mapping (SLAM) is a crucial component of unmanned systems, playing a key role in autonomous navigation. Currently, most LiDAR SLAM methods are focused on structured environments. However, highly irregular off-road terrain poses more challenges for LiDAR SLAM tasks, but these environments are not fully represented in existing datasets. To address this issue, we introduce the first dedicated LiDAR SLAM benchmark dataset for off-road environments, named Jlurobot Off-Road Dadaset (JORD). This dataset is collected using a custom avenger data collection platform in large-scale forest off-road scenes, consisting of 8 LiDAR sequences with a total length of approximately 6.07 kilometers, containing 49,144 point cloud frames along with accurate 6DoF ground truth. The dataset includes multiple revisit information within the sequences, making it suitable for LiDAR place recognition and SLAM tasks. Furthermore, we employe several state-of-the-art methods for benchmarking to validate the dataset's challenges. The release of JORD aims to provide researchers with valuable resources to develop new approaches and explore novel directions for unmanned systems in off-road environments. The complete dataset and code is available at https://github.com/jiurobots/JORD.