Planar Velocity Estimation for Fast-Moving Mobile Robots Using Event-Based Optical Flow
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Date
2025-07
Publication Type
Journal Article
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yes
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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 32 m/s further confirm the effectiveness of our approach, indicating significant potential for real-world deployment.
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published
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Journal / series
Volume
10 (7)
Pages / Article No.
7318 - 7325
Publisher
IEEE
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Subject
Advanced driver assistance systems; optical flow; state estimation; velocity measurement; visual odometry
Organisational unit
01225 - D-ITET Zentr. f. projektbasiertes Lernen / D-ITET Center for Project-Based Learning
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Is new version of: https://doi.org/10.48550/arXiv.2505.11116