A Faster, Lighter and Stronger Deep Learning-Based Approach for Place Recognition

TL;DR

This paper introduces a novel deep learning approach for visual place recognition that is faster, lighter, and more accurate, utilizing a new backbone network and a trainable feature matcher to outperform existing methods.

Contribution

The authors propose RepVGG-lite as a new backbone network and a trainable attention-based feature matcher, significantly reducing model size and inference time while improving accuracy in place recognition.

Findings

14x fewer parameters than Patch-NetVLAD

6.8x lower FLOPs than Patch-NetVLAD

0.5% higher Recall@1 than Patch-NetVLAD

Abstract

Visual Place Recognition is an essential component of systems for camera localization and loop closure detection, and it has attracted widespread interest in multiple domains such as computer vision, robotics and AR/VR. In this work, we propose a faster, lighter and stronger approach that can generate models with fewer parameters and can spend less time in the inference stage. We designed RepVGG-lite as the backbone network in our architecture, it is more discriminative than other general networks in the Place Recognition task. RepVGG-lite has more speed advantages while achieving higher performance. We extract only one scale patch-level descriptors from global descriptors in the feature extraction stage. Then we design a trainable feature matcher to exploit both spatial relationships of the features and their visual appearance, which is based on the attention mechanism. Comprehensive experiments on challenging benchmark datasets demonstrate the proposed method outperforming recent other state-of-the-art learned approaches, and achieving even higher inference speed. Our system has 14 times less params than Patch-NetVLAD, 6.8 times lower theoretical FLOPs, and run faster 21 and 33 times in feature extraction and feature matching. Moreover, the performance of our approach is 0.5\% better than Patch-NetVLAD in Recall@1. We used subsets of Mapillary Street Level Sequences dataset to conduct experiments for all other challenging conditions.