Minimalistic optic flow sensors applied to indoor and outdoor visual guidance and odometry on a car-like robot

Abstract

Here we present a novel bio-inspired optic flow (OF) sensor and its application to visual guidance and odometry on a low-cost car-like robot called BioCarBot. The minimalistic OF sensor was robust to high-dynamic-range lighting conditions and to various visual patterns encountered thanks to its M2APIX auto-adaptive pixels and the new cross-correlation OF algorithm implemented. The low-cost car-like robot estimated its velocity and steering angle, and therefore its position and orientation, via an extended Kalman filter (EKF) using only two downward-facing OF sensors and the Ackerman steering model. Indoor and outdoor experiments were carried out in which the robot was driven in the closed-loop mode based on the velocity and steering angle estimates. The experimental results obtained show that our novel OF sensor can deliver high-frequency measurements () in a wide OF range (1.5–) and in a 7-decade high-dynamic light level range. The OF resolution was constant and could be adjusted as required (up to ), and the OF precision obtained was relatively high (standard deviation of with an average OF of , under the most demanding lighting conditions). An EKF-based algorithm gave the robot’s position and orientation with a relatively high accuracy (maximum errors outdoors at a very low light level: and over about and ) despite the low-resolution control systems of the steering servo and the DC motor, as well as a simplified model identification and calibration. Finally, the minimalistic OF-based odometry results were compared to those obtained using measurements based on an inertial measurement unit (IMU) and a motor’s speed sensor.