Event-driven neuromorphic vision enables energy-efficient visual place recognition

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 neuromorphic framework that leverages event-based cameras and spiking neural networks (SNNs) to learn compact, invariant place descriptors from a limited number of exemplars, enabling robust VPR across severe 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. Across three challenging benchmarks (Brisbane-Event-VPR, NSAVP and NYC-Event-VPR), SpikeVPR matches the performance of state-of-the-art deep networks while reducing model size by 50 × and lowering estimated energy consumption by 30–250 × , based on an analytical energy model, enabling efficient real-time deployment on mobile and neuromorphic hardware. These results demonstrate that spike-based coding offers an efficient pathway toward robust VPR in complex, changing environments.