Revisiting A Simple MLP Framework for Z-axis Rotation-invariant Point Cloud Place Recognition

Abstract The objective of point cloud place recognition is to convert a point cloud into a global descriptor that can be utilized in autonomous driving applications to identify the best-matched road scene from an extensive dataset. However, capturing a point cloud from an arbitrary view by robots or self-driving vehicles often involves scene rotations, making existing deep learning-based methods susceptible to errors.To quantify this performance degradation, we introduce a novel metric: Average Recall@N under arbitrary rotations, denoted as ''R-AR@N''. To address this issue, we propose a Geometrical Transformation Module designed to convert rotation-sensitive coordinates into rotation-invariant representations. Additionally, we observe that the design of overly complex networks may not be crucial for effective point cloud analysis. In line with the straightforward architectural design of PointMLP {pointmlp}, we introduce a Local Feature Transformation module that utilizes statistical representations to transform local point features within a reasonable range. This enables the network to capture diverse geometric structures and generate a robust global descriptor.Our proposed method undergoes extensive evaluation on the Oxford outdoor dataset and three in-house datasets, demonstrating an improvement of at least 2% over previous methods on the newly proposed ''R-AR@N'' metric. Our code is available at https://github.com/jasonwjw/RI-PointMLP