Training a Convolutional Neural Network for Appearance-Invariant Place Recognition

TL;DR

This paper introduces a CNN trained with triplets to recognize revisited locations despite severe appearance changes, improving place recognition for visual SLAM in robotics.

Contribution

The first CNN designed specifically for appearance-invariant place recognition, trained with triplets to handle severe visual variability.

Findings

Outperforms state-of-the-art algorithms on multiple datasets.

Effective in recognizing places under weather and illumination changes.

Demonstrates robustness in challenging visual conditions.

Abstract

Place recognition is one of the most challenging problems in computer vision, and has become a key part in mobile robotics and autonomous driving applications for performing loop closure in visual SLAM systems. Moreover, the difficulty of recognizing a revisited location increases with appearance changes caused, for instance, by weather or illumination variations, which hinders the long-term application of such algorithms in real environments. In this paper we present a convolutional neural network (CNN), trained for the first time with the purpose of recognizing revisited locations under severe appearance changes, which maps images to a low dimensional space where Euclidean distances represent place dissimilarity. In order for the network to learn the desired invariances, we train it with triplets of images selected from datasets which present a challenging variability in visual appearance. The triplets are selected in such way that two samples are from the same location and the third one is taken from a different place. We validate our system through extensive experimentation, where we demonstrate better performance than state-of-art algorithms in a number of popular datasets.