[Paper Review] Collaborative Visual Place Recognition

Yiming Li, Zonglin Lyu+4 more|arXiv (Cornell University)|Oct 9, 2023

Indoor and Outdoor Localization Technologies4 citations

TL;DR

This paper proposes Collaborative Visual Place Recognition (CoVPR), a novel multi-robot framework that enhances place recognition by fusing visual features from nearby agents using similarity-regularized information fusion. The method achieves up to 12% higher recall in urban environments compared to single-agent VPR, with consistent gains in rural (+3%) and indoor (+1%) settings, while maintaining low communication overhead and end-to-end trainable compatibility with models like NetVLAD.

ABSTRACT

Visual place recognition (VPR) capabilities enable autonomous robots to navigate complex environments by discovering the environment's topology based on visual input. Most research efforts focus on enhancing the accuracy and robustness of single-robot VPR but often encounter issues such as occlusion due to individual viewpoints. Despite a number of research on multi-robot metric-based localization, there is a notable gap in research concerning more robust and efficient place-based localization with a multi-robot system. This work proposes collaborative VPR, where multiple robots share abstracted visual features to enhance place recognition capabilities. We also introduce a novel collaborative VPR framework based on similarity-regularized information fusion, reducing irrelevant noise while harnessing valuable data from collaborators. This framework seamlessly integrates with well-established single-robot VPR techniques and supports end-to-end training with a weakly-supervised contrastive loss. We conduct experiments in urban, rural, and indoor scenes, achieving a notable improvement over single-agent VPR in urban environments (~12\%), along with consistent enhancements in rural (~3\%) and indoor (~1\%) scenarios. Our work presents a promising solution to the pressing challenges of VPR, representing a substantial step towards safe and robust autonomous systems.

Motivation & Objective

• To address the limitations of single-robot visual place recognition (VPR), particularly due to occlusions and limited viewpoints, by enabling multi-robot collaboration.
• To develop a robust, scalable, and efficient framework for collaborative topological localization using shared visual features among nearby robots.
• To integrate seamlessly with existing single-agent VPR models like NetVLAD while supporting end-to-end weakly-supervised training.
• To establish a comprehensive benchmark for collaborative VPR across diverse environments, including urban, rural, and indoor settings.

Proposed method

• Proposes a similarity-regularized information fusion module that combines visual descriptors from multiple robots while suppressing irrelevant or noisy features.
• Uses a weakly-supervised contrastive loss to train the entire system end-to-end, enabling joint optimization of feature extraction and fusion.
• Introduces a collaborative VPR framework where each robot receives and fuses features from nearby collaborators within a defined communication radius.
• Employs a re-ranking strategy that combines ranking scores from multiple agents, improving recognition accuracy by leveraging complementary visual perspectives.
• Supports both single-agent and multi-agent evaluation modes, enabling cross-evaluation to validate robustness and convergence.
• Applies average pooling and re-ordering baselines for comparison, demonstrating the superiority of the proposed fusion mechanism.

Experimental results

Research questions

• RQ1 Can multi-robot collaboration significantly improve visual place recognition performance under challenging conditions such as occlusions and viewpoint variations?
• RQ2 How does the distance between collaborating robots affect the performance of collaborative VPR in real-world scenarios?
• RQ3 Can a similarity-regularized fusion mechanism outperform simple averaging or re-ranking in reducing noise while enhancing recognition accuracy?
• RQ4 To what extent can the proposed CoVPR framework be integrated with existing single-agent VPR models like NetVLAD?
• RQ5 How does the method generalize across diverse environments, including urban, rural, and indoor settings?

Key findings

• In urban environments, the proposed CoVPR method achieves a 12% improvement in recall@1 compared to single-agent VPR, with gains of 3% in rural and 1% in indoor settings.
• The method maintains consistent performance gains even when the distance between the ego robot and collaborators increases to 15 meters, demonstrating robustness to range variation.
• At 8 meters, other methods like average pooling and re-ordering degrade in performance, but the proposed method still improves recall by a significant margin.
• Cross-evaluation shows that training in a multi-agent setting (multi-single) leads to better convergence and generalization than single-agent training.
• The ablation study confirms that the similarity-regularized fusion mechanism is more effective than simple averaging or re-ranking, especially under high-distance conditions.
• The method incurs minimal communication overhead and is compatible with established VPR models such as NetVLAD, enabling seamless integration into existing systems.