SSC: Semantic Scan Context for Large-Scale Place Recognition
This work addresses place recognition for SLAM systems in robotics and autonomous vehicles, offering an incremental advancement by incorporating semantics and translation correction into existing methods.
The paper tackles the challenge of place recognition in point clouds by introducing a novel global descriptor, Semantic Scan Context, that leverages semantic information and a two-step global semantic ICP to correct translations, achieving state-of-the-art performance on the KITTI dataset with significant improvements.
Place recognition gives a SLAM system the ability to correct cumulative errors. Unlike images that contain rich texture features, point clouds are almost pure geometric information which makes place recognition based on point clouds challenging. Existing works usually encode low-level features such as coordinate, normal, reflection intensity, etc., as local or global descriptors to represent scenes. Besides, they often ignore the translation between point clouds when matching descriptors. Different from most existing methods, we explore the use of high-level features, namely semantics, to improve the descriptor's representation ability. Also, when matching descriptors, we try to correct the translation between point clouds to improve accuracy. Concretely, we propose a novel global descriptor, Semantic Scan Context, which explores semantic information to represent scenes more effectively. We also present a two-step global semantic ICP to obtain the 3D pose (x, y, yaw) used to align the point cloud to improve matching performance. Our experiments on the KITTI dataset show that our approach outperforms the state-of-the-art methods with a large margin. Our code is available at: https://github.com/lilin-hitcrt/SSC.