Planar Velocity Estimation for Fast-Moving Mobile Robots Using Event-Based Optical Flow (2505.11116v1)

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.

• The paper introduces a novel method using event-based optical flow from event cameras to improve planar velocity estimation for fast-moving robots, addressing limitations of traditional odometry and IMU fusion methods under challenging conditions.
• Experimental results show the proposed event-based method achieves performance parity with state-of-the-art methods and a 38.3% improvement in lateral RMSE error, validated on a 1:10 scale racing platform.
• The approach was successfully tested at speeds up to 32 m/s in real-world scenarios, demonstrating potential for autonomous driving and ADAS with computational efficiency comparable to existing methods, and the algorithm is open-source.

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

The paper "Planar Velocity Estimation for Fast-Moving Mobile Robots Using Event-Based Optical Flow" presents a novel approach to enhancing velocity estimation reliability for mobile robots, diverging from conventional reliance on wheel odometry fused with IMU data. The traditional methods assume non-slip steering or rely heavily on complex vehicle dynamics models, both of which show significant limitations under variable environmental conditions such as slippery roads. This research proposes a paradigm shift by introducing an event-based optical flow (eOF) methodology, leveraging event cameras to overcome the drawbacks of conventional methods, particularly under high-speed scenarios.

Event cameras present a promising alternative to traditional image sensors due to their asynchronous data output and robustness against motion blur, thanks to a high dynamic range and low latency. By utilizing optical flow from these cameras, the developed velocity estimation method significantly reduces the constraints usually imposed by the assumptions of traditional systems. The approach frames velocity estimation as a problem of planar kinematics, a perspective justified by the relatively fixed plane of movement inherent in road vehicles.

The research employs an experimental setup comprising a 1:10 scale autonomous racing platform, validated against precise motion-capture data. Compared against the state-of-the-art event Visual Inertial Odometry (eVIO) methods, the proposed system exhibits a notable performance parity, with an improvement of 38.3% in lateral RMSE error. This illustrates the effectiveness of considering velocity estimates directly, independent of pose graph construction, which in the cases of lateral velocity computation specifically, showcases significant advancement over more complex algorithmic counterparts.

For a deeper understanding, the experimentation includes real-world scenarios at highway speeds, achieving recorded velocities up to 32 ms^-1. In these tests, the methodology substantiated its operational viability and precision, holding potential for application in autonomous driving and advanced driver-assistance systems (ADAS). Beyond theoretical gains, the research underscores practical enhancements using non-standard sensor modalities, implying a robust deployability across varied and demanding environments.

Moreover, the paper acknowledges computational findings, which reveal the CPU and memory load operations to be comparative with existing methodologies, yet maintaining substantial advantages concerning accuracy without the memory and processing demand typical of machine learning-based optical flow estimations.

In conclusion, the advancement in velocity estimation accuracy presented in this paper may prompt an evolution in vehicular perception systems. By eliminating reliance on slip-prone proprioceptive sensors and incorporating robust sensor technologies like event cameras, this research contributes a meaningful step forward. Future exploration into tailored optical flow networks harnessing event data or optimized event camera algorithms might further refine performance metrics, potentially widening the application of advanced sensory technologies in real-world robotics and automated systems. The open-source sharing of the algorithm offers a springboard for further innovation and industry adoption.