Event-based vision for egomotion estimation using precise event timing

TL;DR

This paper introduces a novel event-based egomotion estimation pipeline using precise event timing and spiking neural networks, enabling low-latency, energy-efficient motion tracking suitable for robotics.

Contribution

It presents a fully event-based egomotion estimation method with a spiking neural network that directly processes event streams, eliminating frame-based intermediaries.

Findings

Demonstrates strong potential for low-latency, low-power motion estimation on dedicated hardware.

Achieves state-of-the-art accuracy in egomotion estimation with event-based sensors.

Shows promise for real-time robotics applications.

Abstract

Egomotion estimation is crucial for applications such as autonomous navigation and robotics, where accurate and real-time motion tracking is required. However, traditional methods relying on inertial sensors are highly sensitive to external conditions, and suffer from drifts leading to large inaccuracies over long distances. Vision-based methods, particularly those utilising event-based vision sensors, provide an efficient alternative by capturing data only when changes are perceived in the scene. This approach minimises power consumption while delivering high-speed, low-latency feedback. In this work, we propose a fully event-based pipeline for egomotion estimation that processes the event stream directly within the event-based domain. This method eliminates the need for frame-based intermediaries, allowing for low-latency and energy-efficient motion estimation. We construct a shallow spiking neural network using a synaptic gating mechanism to convert precise event timing into bursts of spikes. These spikes encode local optical flow velocities, and the network provides an event-based readout of egomotion. We evaluate the network's performance on a dedicated chip, demonstrating strong potential for low-latency, low-power motion estimation. Additionally, simulations of larger networks show that the system achieves state-of-the-art accuracy in egomotion estimation tasks with event-based cameras, making it a promising solution for real-time, power-constrained robotics applications.