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High-quality datasets can speed up breakthroughs and reveal potentialdeveloping directions in SLAM research. To support the research on corner casesof visual SLAM systems, this paper presents Ground-Challenge: a challengingdataset comprising 36 trajectories with diverse corner cases such as aggressivemotion, severe occlusion, changing illumination, few textures, pure rotation,motion blur, wheel suspension, etc. The dataset was collected by a ground robotwith multiple sensors including an RGB-D camera, an inertial measurement unit(IMU), a wheel odometer and a 3D LiDAR. All of these sensors werewell-calibrated and synchronized, and their data were recorded simultaneously.To evaluate the performance of cutting-edge SLAM systems, we tested them on ourdataset and demonstrated that these systems are prone to drift and fail onspecific sequences. We will release the full dataset and relevant materialsupon paper publication to benefit the research community. For more information,visit our project website at https://github.com/sjtuyinjie/Ground-Challenge.

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