Ground-Challenge

A Multi-sensor SLAM Dataset Focusing on Corner Cases for Ground Robots

Figure 1. Different corner cases for SLAM

Notice:

We strongly recommend that the newly proposed SLAM algorithm be tested on our Ground-Challenge benchmark, because our data has following features:

1. A rich pool of sensory information including RGBD, wheel, IMU and so on.

2. This benchmark includes diverse corner cases such as aggressive motion, severe occlusion, changing illumination, few textures, pure rotation, motion blur, wheel suspension, etc.

3. This benchmark brings great challenge to existing cutting-edge SLAM algorithms including VINS-Mono, ORB-SLAM3, VINS-RGBD, VIW-Fusion and TartanVO. If your proposed algorihm outperforms SOTA systems on this dataset, your paper will be much more convincing and valuable.

License

The paper link is here.If you use Ground-Challenge in an academic work, please cite:

@inproceedings{yin2023ground,
  title={Ground-challenge: A multi-sensor slam dataset focusing on corner cases for ground robots},
  author={Yin, Jie and Yin, Hao and Liang, Conghui and Jiang, Haitao and Zhang, Zhengyou},
  booktitle={2023 IEEE International Conference on Robotics and Biomimetics (ROBIO)},
  pages={1--5},
  year={2023},
  organization={IEEE}
}

ABSTRACT:

We introduce Ground-Challenge: a novel dataset collected by a ground robot with multiple sensors including an RGB-D camera, an inertial measurement unit (IMU), a wheel odometer and a 3D LiDAR to support the research on corner cases of visual SLAM systems. Our dataset comprises 36 trajectories with diverse corner cases such as aggressive motion, severe occlusion, changing illumination, few textures, pure rotation, motion blur, wheel suspension, etc. Some state-of-the-art SLAM algorithms are tested on our dataset, showing that these systems are seriously drifting and even failing on specific sequences. We will release the dataset and relevant materials upon paper publication to benefit the research community.

1.SENSOR SETUP

1.1 Acquisition Platform

The ground robot is given below. The unit of the figures is centimeter.

Figure 2. The data capture robot.

1.2 Sensor parameters

All the sensors and track devices and their most important parameters are listed as below:

• LIDAR Velodyne VLP-16, 360 Horizontal Field of View (FOV),-30 to +10 vertical FOV,10Hz,Max Range 200 m,Range Resolution 3 cm, Horizontal Angular Resolution 0.2°.

• V-I Sensor,Realsense d435i,RGB/Depth 640*480,69H-FOV,42.5V-FOV,15Hz;IMU 6-axix, 200Hz

• IMU,Xsens Mti-300,9-axis,400Hz;

• Wheel Odometer,AgileX,2D,25Hz;

The rostopics of our rosbag sequences are listed as follows:

• LIDAR: /velodyne_points

• V-I Sensor:
  /camera/color/image_raw ,
  /camera/depth/image_raw ,
  /camera/imu

• IMU: /imu/data

• Wheel Odometer: /odom

2.DATASET SEQUENCES

An overview of Ground-Challenge is given in the table below:

Scenario	Darkroom	Occlusion	Office	Room	Wall	Motionblur	Hall	Loop	Roughroad	Corridor	Rotation	Static	Slope	TOTAL
Number	3	4	3	3	3	3	3	2	3	2	3	2	2	36
Dist/m	92.0	273.8	75.5	102.1	86.7	166.6	236.3	371.8	68.1	164.3	12.4	1.9	128.5	1780.0
Duration/s	203.6	334.2	164.0	154.7	189.3	145.5	302.4	332.7	186.3	198.1	183.2	92.6	195.0	2681.6
Size/GB	6.1	9.9	4.7	4.6	5.6	4.3	8.7	9.9	5.4	5.8	5.4	2.7	5.7	78.8

2.1 Visual Challenges

Sequence Name	Total Size	Duration	Features	Rosbag
Darkroom1	2.9g	100s	slight light, going into a room	Rosbag
Darkroom2	2.3g	76s	sharp turn	Rosbag
Darkroom3	1.9g	64s	slight light	Rosbag
Occlusion1	2.9g	97s	moving feet, far features	Rosbag
Occlusion2	3.2g	108s	hand occlusion	Rosbag
Occlusion3	2.6g	89s	hand occlusion	Rosbag
Occlusion4	1.2g	40s	complete occlusion	Rosbag
Office1	1.3g	46s	exposure change	Rosbag
Office2	1.9g	66s	going into a dark room	Rosbag
Office3	1.5g	52s	office	Rosbag
Room1	1.3g	46s	exposure change	Rosbag
Room2	1.9g	66s	going into a dark room	Rosbag
Room3	1.5g	52s	office	Rosbag
Motionblur1	1.5g	52s	aggressive motion	Rosbag
Motionblur2	1.6g	54s	aggressive motion	Rosbag
Motionblur3	1.2g	40s	aggressive motion	Rosbag
Wall1	1.7g	59s	wall in a corridor	Rosbag
Wall2	2.0g	66s	wall in a big hall	Rosbag
Wall3	3.9g	65s	wall in a corridor	Rosbag

2.2 Wheel Challenge

Sequence Name	Total Size	Duration	Features	Rosbag
Hall1	2.6g	91s	slippery ground, a reflective surface	Rosbag
Hall2	3.2g	110s	slippery ground, a reflective surface	Rosbag
Hall3	2.9g	101s	slippery ground, walking human	Rosbag
Loop1	4.1g	97s	moving feet, far features	Rosbag
Loop2	5.8g	137s	hand occlusion	Rosbag
Roughroad1	2.2g	75s	rough road	Rosbag
Roughroad2	1.5g	52s	rough road	Rosbag
Roughroad3	1.8g	59s	rough road	Rosbag

2.3 Specific Movement Patterns

Sequence Name	Total Size	Duration	Features	Rosbag
Corridor1	2.9g	100s	zigzag, long corridor	Rosbag
Corridor2	2.9g	98s	straight forward, long corridor	Rosbag
Rotation1	1.6g	53s	moving feet, far features	Rosbag
Rotation2	2.1g	73s	hand occlusion	Rosbag
Rotation3	1.7g	57s	rough road	Rosbag
Static1	1.6g	56s	rough road	Rosbag
Static2	1.1g	37s	rough road	Rosbag
Slope1	2.8g	96s	slope	Rosbag
Slope2	2.9g	99s	slope	Rosbag

3. Configuration Files

We provide configuration files for several cutting-edge baseline methods, including VINS-RGBD,TartanVO,VINS-Mono and VIW-Fusion.

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