Place Recognition in Gardens by Learning Visual Representations: Data Set and Benchmark Analysis

María Leyva-Vallina*, Nicola Strisciuglio, Nicolai Petkov

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

Visual place recognition is an important component of systems for camera localization and loop closure detection. It concerns the recognition of a previously visited place based on visual cues only. Although it is a widely studied problem for indoor and urban environments, the recent use of robots for automation of agricultural and gardening tasks has created new problems, due to the challenging appearance of garden-like environments. Garden scenes predominantly contain green colors, as well as repetitive patterns and textures. The lack of available data recorded in gardens and natural environments makes the improvement of visual localization algorithms difficult. In this paper we propose an extended version of the TB-Places data set, which is designed for testing algorithms for visual place recognition. It contains images with ground truth camera pose recorded in real gardens in different seasons, with varying light conditions. We constructed and released a ground truth for all possible pairs of images, indicating whether they depict the same place or not. We present the results of a benchmark analysis of methods based on convolutional neural networks for holistic image description and place recognition. We train existing networks (i.e. ResNet, DenseNet and VGG NetVLAD) as backbone of a two-way architecture with a contrastive loss function. The results that we obtained demonstrate that learning garden-tailored representations contribute to an improvement of performance, although the generalization capabilities are limited.

Original languageEnglish
Title of host publicationComputer Analysis of Images and Patterns
Subtitle of host publication18th International Conference, CAIP 2019, Proceedings
EditorsMario Vento, Gennaro Percannella
PublisherSpringer Verlag
Pages324-335
Number of pages12
ISBN (Electronic)978-3-030-29888-3
ISBN (Print)978-3-030-29887-6
DOIs
Publication statusE-pub ahead of print/First online - 22 Aug 2019
Externally publishedYes
Event18th International Conference on Computer Analysis of Images and Patterns, CAIP 2019 - Salerno, Italy
Duration: 3 Sep 20195 Sep 2019
Conference number: 18

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11678 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference18th International Conference on Computer Analysis of Images and Patterns, CAIP 2019
Abbreviated titleCAIP 2019
CountryItaly
CitySalerno
Period3/09/195/09/19

Keywords

  • Benchmarking
  • Data set
  • Deep learning
  • Place recognition

Fingerprint Dive into the research topics of 'Place Recognition in Gardens by Learning Visual Representations: Data Set and Benchmark Analysis'. Together they form a unique fingerprint.

  • Cite this

    Leyva-Vallina, M., Strisciuglio, N., & Petkov, N. (2019). Place Recognition in Gardens by Learning Visual Representations: Data Set and Benchmark Analysis. In M. Vento, & G. Percannella (Eds.), Computer Analysis of Images and Patterns: 18th International Conference, CAIP 2019, Proceedings (pp. 324-335). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11678 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-29888-3_26