Hard Point Cloud Localization Dataset

This is a dataset to evaluate the robustness of point-cloud-based localization algorithms in extremely severe situations including aggressive sensor motion, point cloud degeneration, and data interruptions.
The dataset contains five indoor sequences recorded with a Microsoft Azure Kinect and three outdoor sequences recorded with a Livox MID360. Each outdoor sequence is split into two consecutive rosbags.
indoor_easy_01 & 02    : "Easy" sequences without aggressive sensor motion and data interruptions.indoor_hard_01             : "Hard" sequence involving quick sensor motion and point cloud degeneration.indoor_kidnap_01 & 02 : Sequences that involve kidnapping situations (the sensor was moved to another room while its view was completely occluded several times)outdoor_hard_01 & 02  : "Hard" outdoor sequence involving quick sensor motion and point cloud degeneration.outdoor_kidnap             : Outdoor sequence that involve kidnapping situations.Each rosbag corresponds to an environmental map file as follows:indoor_easy       : map_indoor_easy.plyindoor_kidnap    : map_indoor_easy.plyindoor_hard        : map_indoor_hard.plyoutdoor_kidnap  : map_outdoor_kidnap.plyoutdoor_hard     : map_outdoor_hard.plyEach indoor sequence contains the following ROS2 messages:- /points2/decompressed : sensor_msgs/msg/PointCloud2 : Point cloud data- /imu                                : sensor_msgs/msg/Imu              : Imu dataEach outdoor sequence contains the following ROS2 messages:- /livox/points           : sensor_msgs/msg/PointCloud2       : Point cloud data- /livox/lidar              : livox_ros_driver2/msg/CustomMsg : Point cloud data in the Livox format- /livox/imu               : sensor_msgs/msg/Imu                    : Imu dataWe used the follwoing LiDAR-IMU transformation parameters (T_lidar_imu : [tx, ty, tz, qx, qy, qz, qw]) for the indoor and outdoor sequences:- indoor  : [0.003, 0.004, -0.051, -0.476, 0.474, 0.524, 0.525]- outdoor: [0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 1.000]Groundtruth trajectories were obtained through batch optimization of point cloud registration residuals and IMU motion residuals. Each line in a GT trajectory file represents the LiDAR pose in the map frame [time, tx, ty, tz, qx, qy, qz, qw] (TUM format). We recommend evo toolkit (https://github.com/MichaelGrupp/evo) for quantitative evaluation.