TartanDrive 2.0

Seven-hour off-road driving dataset collected on a Yamaha Viking ATV at speeds up to 15 m/s, adding three LiDAR sensors to the original camera, IMU, GPS and proprioceptive data, with tooling to reconfigure the data for self-supervised off-road learning.

TartanDrive 2.0 is an expanded large-scale off-road driving dataset from CMU's AirLab, collected on a Yamaha Viking All-Terrain Vehicle (ATV named 'Vicky') over 255+ acres of diverse terrain in Western Pennsylvania (narrow paths, dense foliage, rocky areas, dirt paths, steep hills). It comprises roughly 7 hours of data at speeds up to 15 m/s. Building on TartanDrive 1.0, version 2.0 adds three LiDAR sensors: two Velodyne VLP-32 units (front-roof mounted, one tilted downward) and one Livox Mid-70 (under the camera), all at 10 Hz. The camera system is a Carnegie Robotics MultiSense S21 providing stereo greyscale images plus a left RGB image at 10 Hz. Inertial/positioning comes from a NovAtel PROPAK-V3-RT2i GNSS (IMU at 100 Hz, GPS-fused pose at 50 Hz) and the MultiSense IMU at 400 Hz. Proprioceptive data (Racepak G2X Pro logger plus joystick/pedals) includes per-wheel RPM, rear suspension shock travel, steering commands and angle, and accelerator/brake pedal positions. Post-processed products include TartanVO and Super Odometry estimates, registered point clouds, predicted point clouds, heightmaps, RGB bird's-eye-view maps, IMU-derived roughness cost, and 200x200m BEV LiDAR feature maps at 0.5m resolution (min/max/mean height, roughness, SVD features, ground height/slope). Rich per-sequence metadata (driver ID, vehicle, passenger count, date/time, weather, lighting, course ID, top/average speed, duration) plus timestamped event annotations and 1-5 driving-quality scores are included. Data is distributed both as original rosbags and in a KITTI-like time-synced sequence format, with scripts to regenerate data at custom sample frequency, map size and resolution. A GUI tool (scripts/tartandrive_gui.py) lets users browse runs and select which modalities to download to save storage; a point-cloud scan of the ATV is downloadable separately. Released alongside the ICRA 2024 paper (arXiv:2402.01913).