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

arXiv: https://arxiv.org/abs/2408.16754 · HTML: https://mlsanity.com/paper/2408.16754/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Aug 2024.

## Built on by (most cited first, 9 of 9)

- [Ensemble-Based Event Camera Place Recognition Under Varying Illumination](https://mlsanity.com/paper/2509.01968/) (2025)
- [Event-Driven Neuromorphic Vision Enables Energy-Efficient Visual Place Recognition](https://mlsanity.com/paper/2604.03277/) (2026)
- [EventGeM: Global-to-Local Feature Matching for Event-Based Visual Place Recognition](https://mlsanity.com/paper/2603.05807/) (2026)
- [Event-Based Visual Teach-and-Repeat via Fast Fourier-Domain Cross-Correlation](https://mlsanity.com/paper/2509.17287/) (2025)
- [Realizing Fully-Integrated, Low-Power, Event-Based Pupil Tracking with Neuromorphic Hardware](https://mlsanity.com/paper/2511.20175/) (2025)
- [Unbiased Gradient Estimation for Event Binning via Functional Backpropagation](https://mlsanity.com/paper/2602.12590/) (2026)
- [Robust PnP on a Neuromorphic Processor for Object Pose Estimation](https://mlsanity.com/paper/2607.16834/) (2026)
- [Multi-viewpoint Geo-localization with Event Cameras](https://mlsanity.com/paper/2609.21219/) (2026)
- [Removing the Compute Bottleneck for Event-based Visual Place Recognition with Spiking Neural Networks](https://mlsanity.com/paper/2610.16022/) (2026)

## Abstract (arXiv)

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 bio-realistic networks. In order to overcome this challenge, fusion of hardware and algorithms is critical to employ 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 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 to a benchmark place recognition method, Sum-of-Absolute-Differences (SAD), 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.