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 M(2)APIX 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 (>300 Hz) in a wide OF range (1.5-15 rad s(-1)) and in a 7-decade high-dynamic light level range. The OF resolution was constant and could be adjusted as required (up to 0.05 rad s(-1)), and the OF precision obtained was relatively high (standard deviation of 0.17 rad s(-1) with an average OF of 4.5 rad s(-1), 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: 0.95 mand 0.58 rad over about 32 mand 8 pi rad) 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.