Compact Environment-Invariant Codes for Robust Visual Place Recognition

TL;DR

This paper introduces a supervised hashing approach to create compact, environment-invariant binary codes for visual place recognition, improving robustness and efficiency over existing methods.

Contribution

It proposes a modified VPR pipeline using supervised hashing to generate compact binary codes that are robust to environmental changes, outperforming some deep learning methods.

Findings

Binary codes improve robustness to environmental variations.

Supervised hashing enhances computational efficiency.

Outperforms or matches state-of-the-art deep learning VPR methods.

Abstract

Robust visual place recognition (VPR) requires scene representations that are invariant to various environmental challenges such as seasonal changes and variations due to ambient lighting conditions during day and night. Moreover, a practical VPR system necessitates compact representations of environmental features. To satisfy these requirements, in this paper we suggest a modification to the existing pipeline of VPR systems to incorporate supervised hashing. The modified system learns (in a supervised setting) compact binary codes from image feature descriptors. These binary codes imbibe robustness to the visual variations exposed to it during the training phase, thereby, making the system adaptive to severe environmental changes. Also, incorporating supervised hashing makes VPR computationally more efficient and easy to implement on simple hardware. This is because binary embeddings can be learned over simple-to-compute features and the distance computation is also in the low-dimensional hamming space of binary codes. We have performed experiments on several challenging data sets covering seasonal, illumination and viewpoint variations. We also compare two widely used supervised hashing methods of CCAITQ and MLH and show that this new pipeline out-performs or closely matches the state-of-the-art deep learning VPR methods that are based on high-dimensional features extracted from pre-trained deep convolutional neural networks.