Fast Sequence-Matching Enhanced Viewpoint-Invariant 3-D Place Recognition

Recognizing the same place undervariant viewpoint differences is the fundamental capability for human beings and animals. However, such a strong place recognition ability in robotics is still an unsolved problem. Extracting local invariant descriptors from the same place under various viewpoint differences is difficult. This article seeks to provide robots with a human-like place recognition ability using a new 3-D feature learning method. This article proposes a novel lightweight 3-D place recognition and fast sequence matching to achieve robust 3-D place recognition, capable of recognizing places from a previous trajectory regardless of viewpoints and temporary observation differences. Specifically, we extracted the viewpoint-invariant place feature from 2-D spherical perspectives by leveraging spherical harmonics' orientation-equivalent property. To improve sequence-matching efficiency, we designed a coarse-to-fine fast sequence-matching mechanism to balance the matching efficiency and accuracy. Despite the apparent simplicity, our proposed approach outperforms the relative state of the art. In both public and self-gathered datasets with orientation/translation differences or noise observations, our method can achieve above 95% average recall for the best match with only 18% inference time of PointNet-based place recognition methods.

Automated summary

This article proposes a new 3-D feature learning method to give robots a human-like place recognition ability. The method extracts viewpoint-invariant place features and uses a fast sequence matching mechanism to achieve robust 3-D place recognition. The proposed approach outperforms the existing state of the art, achieving above 95% average recall with only 18% inference time of PointNet-based methods.