  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.
