Neuromorphic computing offers a transformative pathway to overcome the computational and energy challenges faced in deploying robotic localization and navigation systems at the edge. Visual place recognition, a critical component for navigation, is often hampered by the high resource demands of conventional systems, making them unsuitable for small-scale robotic platforms which still require to perform complex, long-range tasks. Although neuromorphic approaches offer potential for greater efficiency, real-time edge deployment remains constrained by the complexity and limited scalability of bio-realistic networks. Here, we demonstrate a neuromorphic localization system that performs accurate place recognition in up to 8km of traversal using models as small as 180 KB with 44k parameters, while consuming less than 1% of the energy required by conventional methods. Our Locational Encoding with Neuromorphic Systems (LENS) integrates spiking neural networks, an event-based dynamic vision sensor, and a neuromorphic processor within a single SPECK(TM) chip, enabling real-time, energy-efficient localization on a hexapod robot. LENS represents the first fully neuromorphic localization system capable of large-scale, on-device deployment, setting a new benchmark for energy efficient robotic place recognition.
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