A compact neuromorphic system for ultra–energy-efficient, on-device robot localization

Abstract

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 accurate long-endurance localization. Although neuromorphic approaches offer potential for greater efficiency, real-time edge deployment remains constrained by the complexity of biorealistic networks. To overcome this challenge, fusion of hardware and algorithms is critical when using this specialized computing paradigm. Here, we demonstrate a neuromorphic localization system that performs competitive place recognition in up to 8 kilometers of traversal using models as small as 180 kilobytes with 44,000 parameters while consuming less than 8% of the energy required by conventional methods. Our system, locational encoding with neuromorphic systems (LENS), integrates spiking neural networks, an event-based dynamic vision sensor, and a neuromorphic processor within a single SynSense Speck chip, enabling real-time, energy-efficient localization on a hexapod robot. When compared with a benchmark place recognition method, sum of absolute differences, LENS performs comparably in overall precision. LENS represents an accurate fully neuromorphic localization system capable of large-scale, on-device deployment for energy-efficient robotic place recognition. Neuromorphic computing enables resource-constrained robots to perform energy-efficient, accurate localization.