The multi-agent scenario consists of four trajectories within the long_corridor scene, each containing segments with inter-agent loop closures.
SeqID | Ground Truth | Color Image | Depth Sensor Image | Depth Proj. Image | Livox | Imu |
---|---|---|---|---|---|---|
Multi-agent_long_corridor | ![]() | Color1 Color2 Color3 Color4 | Depth1 Depth2 Depth3 Depth4 | Lidar1Lidar2Lidar3 Lidar4 | Imu1 Imu2 Imu3 Imu4 |
The sequences are captured as rosbags, which are then compressed with bz2 method. User can uncompress the rosbags for less CPU usage at the cost of 3x memory storage. For user convenience, we extracted data from the rosbag, primarily providing color and depth images, lidar points as PCD files, and IMU.
# | SeqID | Ground Truth | Color Image | Depth Sensor Image | Depth Proj. Image | Livox | Imu |
---|---|---|---|---|---|---|---|
1 | Outdoor_carpark01 | ![]() | Color | Depth Sensor | Depth Proj | Lidar | Imu |
2 | Outdoor_carpark02 | ![]() | Color | Depth Sensor | Depth Proj | Lidar | Imu |
3 | Outdoor_carpark03 | ![]() | Color | Depth Sensor | Depth Proj | Lidar | Imu |
# | SeqID | Ground Truth | Color Image | Depth Sensor Image | Depth Proj. Image | Livox | Imu |
---|---|---|---|---|---|---|---|
1 | Outdoor_campus01 | ![]() | Color | Depth Sensor | Depth Proj | Lidar | Imu |
2 | Outdoor_campus02 | ![]() | Color | Depth Sensor | Depth Proj | Lidar | Imu |
3 | Outdoor_campus03 | ![]() | Color | Depth Sensor | Depth Proj | Lidar | Imu |