ERINet: Effective Rotation Invariant Network for Point Cloud based Place Recognition

Abstract

Place recognition task is a crucial part of 3D scene recognition in various applications. Nowadays, learning-based point cloud place recognition approaches have achieved remarkable success. However, these methods seldom consider the possible rotation of point cloud data in large-scale real-world place recognition tasks. To cope with this problem, in this work, we propose a novel effective rotation invariant network for large-scale place recognition named ERINet, which captures the recent successful deep network architecture and benefits from holding the rotation-invariant property of point clouds. In this network, we design a core effective rotation invariant module, which enhances the ability to extract rotation-invariant features of 3D point clouds. The benchmark experiments illustrate that our network boosts the performance of the recent works on all evaluation metrics with various rotations, even under challenging rotation cases.