  High-quality datasets can speed up breakthroughs and reveal potential
developing directions in SLAM research. To support the research on corner cases
of visual SLAM systems, this paper presents Ground-Challenge: a challenging
dataset comprising 36 trajectories with diverse corner cases such as aggressive
motion, severe occlusion, changing illumination, few textures, pure rotation,
motion blur, wheel suspension, etc. The dataset was 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. All of these sensors were
well-calibrated and synchronized, and their data were recorded simultaneously.
To evaluate the performance of cutting-edge SLAM systems, we tested them on our
dataset and demonstrated that these systems are prone to drift and fail on
specific sequences. We will release the full dataset and relevant materials
upon paper publication to benefit the research community. For more information,
visit our project website at https://github.com/sjtuyinjie/Ground-Challenge.
