Planar Velocity Estimation for Fast-Moving Mobile Robots Using Event-Based Optical Flow

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 $\mu$-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 ${\text{32}} \,{\text{m}}/{\text{s}}$ further confirm the effectiveness of our approach, indicating significant potential for real-world deployment.