Levelling the Playing Field: A Comprehensive Comparison of Visual Place Recognition Approaches under Changing Conditions

Abstract

In recent years there has been significant improvement in the capability of Visual Place Recognition (VPR) methods, building on the success of both hand-crafted and learnt visual features, temporal filtering and usage of semantic scene information. The wide range of approaches and the relatively recent growth in interest in the field has meant that a wide range of datasets and assessment methodologies have been proposed, often with a focus only on precision-recall type metrics, making comparison difficult. In this paper we present a comprehensive approach to evaluating the performance of 10 state-of-the-art recently-developed VPR techniques, which utilizes three standardized metrics: (a) Matching Performance b) Matching Time c) Memory Footprint. Together this analysis provides an up-to-date and widely encompassing snapshot of the various strengths and weaknesses of contemporary approaches to the VPR problem. The aim of this work is to help move this particular research field towards a more mature and unified approach to the problem, enabling better comparison and hence more progress to be made in future research.

arXiv commentICRA 2019 Workshop on Database Generation and Benchmarking of SLAM Algorithms for Robotics and VR/AR

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Cited by in corpus (11)

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• VPR-Bench: An Open-Source Visual Place Recognition Evaluation Framework with Quantifiable Viewpoint and Appearance Change
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• Binary Neural Networks for Memory-Efficient and Effective Visual Place Recognition in Changing Environments
• Are State-of-the-art Visual Place Recognition Techniques any Good for Aerial Robotics?
• MVP: Unified Motion and Visual Self-Supervised Learning for Large-Scale Robotic Navigation
• ConvSequential-SLAM: A Sequence-based, Training-less Visual Place Recognition Technique for Changing Environments
• Sequence-Based Filtering for Visual Route-Based Navigation: Analysing the Benefits, Trade-offs and Design Choices
• CAMAL: Context-Aware Multi-layer Attention framework for Lightweight Environment Invariant Visual Place Recognition
• A Benchmark Comparison of Visual Place Recognition Techniques for Resource-Constrained Embedded Platforms
• Scene Retrieval for Contextual Visual Mapping