Seed: A Segmentation-Based Egocentric 3D Point Cloud Descriptor for Loop Closure Detection

Place recognition is essential for SLAM system since it is critical for loop closure and can help to correct the accumulated drift and result in a globally consistent map. Unlike the visual slam which can use diverse feature detection methods to describe the scene, there are limited works reported to represent a place using single LiDAR scan. In this paper, we propose a segmentation-based egocentric descriptor termed Seed by using a single LiDAR scan to describe the scene. Through the segmentation approach, we first obtain different segmented objects, which can reduce the noise and resolution effect, making it more robust. Then, the topological information of the segmented objects is encoded into the descriptor. Unlike other reported approaches, the proposed method is rotation invariant and insensitive to translation variation. The feasibility of proposed method is evaluated through the KITTI dataset and the results show that the proposed method outperforms the state-of-the-art method in terms of accuracy.