CAMAL: Context-Aware Multi-layer Attention framework for Lightweight Environment Invariant Visual Place Recognition

Abstract

In the last few years, Deep Convolutional Neural Networks (D-CNNs) have shown state-of-the-art (SOTA) performance for Visual Place Recognition (VPR), a pivotal component of long-term intelligent robotic vision (vision-aware localization and navigation systems). The prestigious generalization power of D-CNNs gained upon training on large scale places datasets and learned persistent image regions which are found to be robust for specific place recognition under changing conditions and camera viewpoints. However, against the computation and power intensive D-CNNs based VPR algorithms that are employed to determine the approximate location of resource-constrained mobile robots, lightweight VPR techniques are preferred. This paper presents a computation- and energy-efficient CAMAL framework that captures place-specific multi-layer convolutional attentions efficient for environment invariant-VPR. At 4x lesser power consumption, evaluating the proposed VPR framework on challenging benchmark place recognition datasets reveal better and comparable Area under Precision-Recall (AUC-PR) curves with approximately 4x improved image retrieval performance over the contemporary VPR methodologies.

arXiv commentunder-review

References in corpus (5)

• Convolutional Neural Network-based Place Recognition
• On the Performance of ConvNet Features for Place Recognition
• Condition-Invariant Multi-View Place Recognition
• Levelling the Playing Field: A Comprehensive Comparison of Visual Place Recognition Approaches under Changing Conditions
• Are State-of-the-art Visual Place Recognition Techniques any Good for Aerial Robotics?

Cited by in corpus (2)

• LCPR: A Multi-Scale Attention-Based LiDAR-Camera Fusion Network for Place Recognition
• Binary Neural Networks for Memory-Efficient and Effective Visual Place Recognition in Changing Environments