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In the last few years, Deep Convolutional Neural Networks (D-CNNs) have shownstate-of-the-art (SOTA) performance for Visual Place Recognition (VPR), apivotal component of long-term intelligent robotic vision (vision-awarelocalization and navigation systems). The prestigious generalization power ofD-CNNs gained upon training on large scale places datasets and learnedpersistent image regions which are found to be robust for specific placerecognition under changing conditions and camera viewpoints. However, againstthe computation and power intensive D-CNNs based VPR algorithms that areemployed to determine the approximate location of resource-constrained mobilerobots, lightweight VPR techniques are preferred. This paper presents acomputation- and energy-efficient CAMAL framework that captures place-specificmulti-layer convolutional attentions efficient for environment invariant-VPR.At 4x lesser power consumption, evaluating the proposed VPR framework onchallenging benchmark place recognition datasets reveal better and comparableArea under Precision-Recall (AUC-PR) curves with approximately 4x improvedimage retrieval performance over the contemporary VPR methodologies.

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