THOR - people tracks

THÖR is a dataset with human motion trajectory and eye gaze data collected in an indoor environment with accurate ground truth for the position, head orientation, gaze direction, social grouping and goals. THÖR contains sensor data collected by a 3D lidar sensor and involves a mobile robot navigating the space. In comparison to other, our dataset has a larger variety in human motion behaviour, is less noisy, and contains annotations at higher frequencies. The dataset includes 13 separate recordings in 3 variations: ``One obstacle" - features one obstacle in the environment and no robot ``Moving robot" - features one obstacle in the environment and the moving robot ``Three obstacles" - features three obstacles in the environment and no robot THOR - people tracks is the part of THÖR data set containing ground truth position of people in the environment, including information about head orientation.  The data are available in three formats: mat - Matlab binary file TSV - text file bag - ROS bag file MAT files File - [char] Path to original QTM file Timestamp - [string] Date and time of the startof the data collection Start Fram - [char] 1 Frames - [double] Number of frames in the file FrameRate - [double] Number of frames per second Events - [struct] 0 Trajectories - [struct] 3D postion of observed reflective markers Labeled  - [struct] Markers belonging to the tracked agents: Count - [double] Number of tracked markers Labels - [cell] List of marker labels Data - [double] Array of dimension {Count}x4x{Frames}, contains the 3D position of each marker and residue RigidBodies - [struct] 6D pose of the helmet, corresponds to head poistion and orientation: Bodies - [double] Number of tracked bodies Name  - [cell] Bodies Names Positions - [double] Array of dimension {Bodies}x3x{Frames} contains the position of the centre of the mass of the markers defining the rigid body Rotations - [double] Array of dimension {Bodies}x9x{Frames} contains rotation matrix describing the orientation of the rigid body RPYs  - [double] Array of dimension {Bodies}x3x{Frames} contains orientation of the rigid body described as RPY angles Residual - [double] Array of dimension {Bodies}x1x{Frames} contains residual for each rigid body TSV files 3D data File Header NO_OF_FRAMES  - number of frames in the file   NO_OF_CAMERAS - number of cameras tracking makers NO_OF_MARKERS - number of tracked markers FREQUENCY - tracking frequency [Hz]    NO_OF_ANALOG - number of analog inputs    ANALOG_FREQUENCY - frequency of analog input    DESCRIPTION -  -- TIME_STAMP - the beginning of the data recording DATA_INCLUDED - the type of data included MARKER_NAMES - names of tracked makers Column names Frame - frame ID Time - frame timestamp [marker name] [C] - coordinate of a [marker name] along [C] axis 6D data File Header NO_OF_FRAMES  - number of frames in the file   NO_OF_CAMERAS - number of cameras tracking makers NO_OF_MARKERS - number of tracked markers FREQUENCY - tracking frequency [Hz]    NO_OF_ANALOG - number of analog inputs    ANALOG_FREQUENCY - frequency of analog input    DESCRIPTION -  -- TIME_STAMP - the beginning of the data recording DATA_INCLUDED - the type of data included BODY_NAMES - names of tracked rigid bodies Colum Names Frame - frame ID Time - frame timestamp The columns are grouped according to the rigid body. Each group starts with the name of the rigid body and then is followed by the position of the centre of the mas and the orientation expressed as RPY angles and rotation matrix Reference: For more details check project website thor.oru.se or check our publications: @article{thorDataset2019, title={TH\"OR: Human-Robot Indoor Navigation Experiment and Accurate Motion Trajectories Dataset}, author={Andrey Rudenko and Tomasz P. Kucner and Chittaranjan S. Swaminathan and Ravi T. Chadalavada and Kai O. Arras and Achim J. Lilienthal}, journal={arXiv preprint arXiv:1909.04403}, year={2019} }