PanoVILD: a challenging panoramic vision, inertial and LiDAR dataset for simultaneous localization and mapping

This paper presents a challenging panoramic vision and LiDAR dataset collected by an autonomous vehicle at Chungbuk National University campus to facilitate robotics research. The vehicle is equipped with a Point Grey Ladybug 3 camera, 3D-LiDAR, global positioning system (GPS) and inertial measurement unit (IMU). The data are collected while driving in an outdoor environment, which includes various scenes such as parking lot, semi-off road path and the campus road scene with traffic. The data from all sensors mounted on the vehicle are timely registered and synchronized. The dataset includes point clouds from 3D LiDAR, images, GPS and IMU measurement. The vision data contain multiple fisheye images covering 360 field-of-view from individual cameras of Ladybug3 at high resolution and accurately stitched spherical panoramic images. The availability of multiple-fisheye and accurate panoramic images may be used for development and validation of novel multi-fisheye, panoramic, and 3D LiDAR based simultaneous localization and mapping (SLAM) systems. The dataset is collected to target various applications such as odometry, SLAM, loop closure detection, deep learning based algorithms with vision, inertial, LiDAR and fusion of visual, inertial and 3D information. To evaluate the algorithm, high accuracy RTK GPS measurements are provided for testing and evaluation.