Abstract
Accurate velocity estimation is critical in mobile robotics, particularly for driver assistance systems and autonomous driving. Wheel odometry fused with Inertial Measurement Unit (IMU) data is a widely used method for velocity estimation; however, it typically requires strong assumptions, such as non-slip steering, or complex vehicle dynamics models that do not hold under varying environmental conditions like slippery surfaces. We introduce an approach to velocity estimation that is decoupled from wheel-to-surface traction assumptions by leveraging planar kinematics in combination with optical flow from event cameras pointed perpendicularly at the ground. The asynchronous micro-second latency and high dynamic range of event cameras make them highly robust to motion blur, a common challenge in vision-based perception techniques for autonomous driving. The proposed method is evaluated through in-field experiments on a 1:10 scale autonomous racing platform and compared to precise motion capture data, demonstrating not only performance on par with the state-of-the-art Event-VIO method but also a 38.3 % improvement in lateral error. Qualitative experiments at highway speeds of up to 32 m/s further confirm the effectiveness of our approach, indicating significant potential for real-world deployment.
Abstract (translated)
准确的速度估计对于移动机器人，尤其是驾驶员辅助系统和自动驾驶至关重要。轮式里程计与惯性测量单元（IMU）数据融合是一种广泛使用的速度估算方法；然而，这种方法通常需要较强的假设条件，例如非打滑转向，或者在不同环境条件下不成立的复杂车辆动力学模型。本文提出了一种独立于轮胎与地面摩擦力假设的速度估算方法，该方法结合了平面运动学和由垂直对准地面的事件相机提供的光学流数据。由于事件相机具有亚毫秒级异步延迟及高动态范围特性，因此它们在自动驾驶视觉感知技术中面对图像模糊时表现出高度鲁棒性。 所提出的这种方法通过在1:10比例自主赛车平台上进行实地实验，并与精确的动作捕捉数据对比进行了评估，不仅展示了与当前最先进的事件-VIO方法相当的性能，而且在横向误差方面提高了38.3%。此外，在高达32米/秒（约115公里/小时）的速度下进行的定性实验进一步证实了该方法的有效性，表明其具有实际应用的巨大潜力。
URL
https://arxiv.org/abs/2505.11116
