Event-Driven Neuromorphic Vision Enables Energy-Efficient Visual Place Recognition

TL;DR

SpikeVPR introduces a neuromorphic, event-driven approach for visual place recognition that is highly energy-efficient and robust to environmental changes, suitable for real-time mobile deployment.

Contribution

The paper presents SpikeVPR, a novel bio-inspired neuromorphic system combining event-based cameras and spiking neural networks for efficient, robust visual place recognition.

Findings

Achieves comparable accuracy to deep networks with 50x fewer parameters.

Consumes 30 to 250 times less energy than traditional methods.

Operates in real-time on mobile and neuromorphic hardware.

Abstract

Reliable visual place recognition (VPR) under dynamic real-world conditions is critical for autonomous robots, yet conventional deep networks remain limited by high computational and energy demands. Inspired by the mammalian navigation system, we introduce SpikeVPR, a bio-inspired and neuromorphic approach combining event-based cameras with spiking neural networks (SNNs) to generate compact, invariant place descriptors from few exemplars, achieving robust recognition under extreme changes in illumination, viewpoint, and appearance. SpikeVPR is trained end-to-end using surrogate gradient learning and incorporates EventDilation, a novel augmentation strategy enhancing robustness to speed and temporal variations. Evaluated on two challenging benchmarks (Brisbane-Event-VPR and NSAVP), SpikeVPR achieves performance comparable to state-of-the-art deep networks while using 50 times fewer parameters and consuming 30 and 250 times less energy, enabling real-time deployment on mobile and neuromorphic platforms. These results demonstrate that spike-based coding offers an efficient pathway toward robust VPR in complex, changing environments.