基于精确事件时序的事件视觉自运动估计
Event-based vision for egomotion estimation using precise event timing
AI总结：
本文提出一种完全基于事件的自运动估计流水线，利用突触门控的浅层脉冲神经网络将精确事件时序转为脉冲爆发以编码光流，在芯片上验证了低延迟低功耗性能，并达到最先进精度。
摘要：
自运动估计对于自主导航和机器人等应用至关重要，这些应用需要精确且实时的运动跟踪。然而，依赖惯性传感器的传统方法对外部条件高度敏感，并且存在漂移问题，导致长距离下出现较大误差。基于视觉的方法，特别是利用事件视觉传感器的方法，通过仅在场景中感知到变化时捕获数据，提供了一种高效的替代方案。这种方法在提供高速、低延迟反馈的同时，最大限度地降低了功耗。在本工作中，我们提出了一种完全基于事件的自运动估计流水线，直接在事件域内处理事件流。该方法消除了对基于帧的中间表示的需求，从而实现了低延迟和节能的运动估计。我们利用突触门控机制构建了一个浅层脉冲神经网络，将精确的事件时序转换为脉冲爆发。这些脉冲编码局部光流速度，网络提供基于事件的自运动读出。我们在专用芯片上评估了网络的性能，展示了其在低延迟、低功耗运动估计方面的强大潜力。此外，对更大网络的模拟表明，该系统在基于事件相机的自运动估计任务中达到了最先进的精度，使其成为实时、功耗受限的机器人应用的一种有前景的解决方案。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
