Ground-Challenge: A Multi-sensor SLAM Dataset Focusing on Corner Cases for Ground Robots
Jie Yin, Hao Yin, Conghui Liang, Haitao Jiang, Zhengyou Zhang
Abstract
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, 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 release the dataset at https://github.com/sjtuyinjie/Ground-Challenge to benefit the research community.