Event-based vision for egomotion estimation using precise event timing

• URL: http://arxiv.org/abs/2501.11554v1
• Date: Mon, 20 Jan 2025 15:41:33 GMT
• Title: Event-based vision for egomotion estimation using precise event timing
• Authors: Hugh Greatorex, Michele Mastella, Madison Cotteret, Ole Richter, Elisabetta Chicca,
• Abstract summary: Egomotion estimation is crucial for applications such as autonomous navigation and robotics. Traditional methods relying on inertial sensors are sensitive to external conditions. Vision-based methods provide an efficient alternative by capturing data only when changes are perceived in the scene.
• Score: 0.6262316762195913
• License:
• 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.

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