Multi-Modal Lidar Dataset for Benchmarking General-Purpose Localization and Mapping Algorithms

Li Qingqing, Yu Xianjia, Jorge Peña Queralta and Tomi Westerlund

This is a novel multi-modal lidar dataset with sensors showcasing different scanning modalities (spinning and solid-state), sensing technologies, and lidar cameras. The focus of the dataset is on low-drift odometry, with ground truth data available in both indoors and outdoors environment with sub-millimeter accuracy from a motion capture (MOCAP) system. For comparison in longer distances, we also include data recorded in larger spaces indoors and outdoors.The dataset contains point cloud data from spinning lidars and solid-state lidars. Also, it provides range images from high resolution spinning lidars, RGB and depth images from a lidar camera, and inertial data from built-in IMUs.


(GitHub repository)