Compact environment-invariant codes for robust visual place recognition

Unnat Jain, Vinay P. Namboodiri, Gaurav Pandey

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

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 [1] and MLH [1] 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.

Original languageEnglish
Title of host publicationProceedings - 2017 14th Conference on Computer and Robot Vision, CRV 2017
PublisherIEEE
Pages40-47
Number of pages8
ISBN (Electronic)9781538628188
DOIs
Publication statusPublished - 7 Feb 2018
Event14th Conference on Computer and Robot Vision, CRV 2017 - Edmonton, Canada
Duration: 17 May 201719 May 2017

Publication series

NameProceedings - 2017 14th Conference on Computer and Robot Vision, CRV 2017
Volume2018-January

Conference

Conference14th Conference on Computer and Robot Vision, CRV 2017
Country/TerritoryCanada
CityEdmonton
Period17/05/1719/05/17

Keywords

  • convolutional neural network
  • dynamic time warping
  • hashing
  • similarity learning
  • visual place recognition

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Signal Processing

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