A compact neuromorphic system for ultra energy-efficient, on-device robot localization
<|reference_start|>A compact neuromorphic system for ultra energy-efficient, on-device robot localization: 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.<|reference_end|>
@article{hines2024a,
    title={A compact neuromorphic system for ultra energy-efficient, on-device
  robot localization},
    author={Adam D. Hines, Michael Milford, Tobias Fischer},
    journal={arXiv preprint arXiv:2408.16754},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.16754},
    primaryClass={cs.RO}
}