Towards a Robust Visual Place Recognition in Large-Scale vSLAM Scenarios Based on a Deep Distance Learning

Area of Science:

• Computer Vision
• Machine Learning
• Robotics

Background:

• Deep learning, particularly Convolutional Neural Networks (CNNs), has advanced visual place recognition for visual Simultaneous Localization and Mapping (vSLAM).
• Challenges in visual place recognition include perceptual aliasing and variability, hindering performance in large-scale vSLAM.
• Traditional deep distance learning methods like triplet loss can be inefficient due to anchor image mining, leading to suboptimal distance relationships.

Purpose of the Study:

• To propose a novel deep distance learning framework for visual place recognition tailored for large-scale vSLAM.
• To address the limitations of traditional deep distance learning methods by introducing a multi-constraint loss function.
• To improve the accuracy and robustness of visual place recognition systems in complex environments.

Main Methods:

• A new deep distance learning framework utilizing a multi-constraint loss function is proposed.
• The framework optimizes distance relationships in Euclidean space by analyzing multiple constraints inherent to visual place recognition.
• It supports various CNN architectures (e.g., AlexNet, VGGNet) for feature extraction.

Main Results:

• The proposed method demonstrated significant performance improvements over traditional deep distance learning, ranging from 19-28%.
• Compared to contemporary methods, performance gains of 40%/36% (VGGNet/AlexNet) on the New College dataset and 27%/24% on the TUM dataset were achieved.
• The framework effectively handles appearance changes in complex environments, validating its robustness.

Conclusions:

• The novel multi-constraint deep distance learning framework offers a superior approach to visual place recognition in vSLAM.
• The method enhances feature distinctiveness and optimizes distance constraints, leading to substantial performance gains.
• The framework's ability to manage environmental variations confirms its practical applicability in real-world scenarios.