RIA-Net: Rotation Invariant Aware 3D Point Cloud for Large-Scale Place Recognition
Wen Hao, Wenjing Zhang, Haonan Su
Abstract
Large-scale place recognition based on point clouds remains a challenge due to the geometric transformations in the scene. Among many geometric transformations, rotation is particularly challenging to handle in practice. Only a few works try to encode rotation invariance into learning based place recognition algorithms. In this paper, we propose a novel rotation-invariant aware network for large-scale place recognition named RIA-Net, which combines semantic and rotation-invariant geometric features to obtain discriminative and generalizable descriptors. First, we design seven dimensional rotation-invariant geometric features based on coordinate differences, distances, and angles in Euclidean space. Moreover, the semantic features in the local region are also considered to enrich the contextual information of scene point clouds. Furthermore, a feature augmentation module is developed to strengthen the rotation-invariant ability of the extracted features. Finally, a novel multi-scale spatial attention module is designed to establish long-range context dependencies for the local descriptors with different dimensions. Experiments on benchmark datasets demonstrate that the proposed RIA-Net outperforms the state-of-the-art place recognition methods. Compared to other rotation-invariant methods, RIA-Net can achieve better results in dealing with rotation problems.