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 MAPIX 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 ([Formula: see text]) in a wide OF range (1.5-[Formula: see text]) and in a 7-decade high-dynamic light level range. The OF resolution was constant and could be adjusted as required (up to [Formula: see text]), and the OF precision obtained was relatively high (standard deviation of [Formula: see text] with an average OF of [Formula: see text], 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: [Formula: see text] and [Formula: see text] over about [Formula: see text] and [Formula: see text]) 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.

Taking Inspiration from Flying Insects to Navigate inside Buildings

“…This functional prototype with its 630 pixels (forming 630 artificial ommatidia) offered a wide view field (180 Â 60 ) over a significant range of lighting conditions and weights~2 g [58] (see twin CurvACE version in Figure 8a). APix stands for Michaelis-Menten auto-adaptive pixel [65]) can auto-adapt in a 7 decade of lighting range and responds appropriately to step changes up to AE3 decades [82]. The pixels do not saturate thanks to the normalization process performed by the very large-scale integration (VLSI) transistors [65]; this is due to the intrinsic properties of the Michaelis-Menten equation [66].…”

Time-of-Travel Methods for Measuring Optical Flow on Board a Micro Flying Robot

“…This algorithm was inspired by the correlation-based models [40,41] and is based on signals’ cross-correlation (Figure 3c), as presented in [26]. First, the pixels’ output signals are digitally filtered with the same band-pass filter used in the thresholding algorithm (see Section 3.1).…”

“…Lastly, the time delay $\Delta t_m$ giving the maximum coefficient ${\varrho }_m$ is obtained and used to compute the OF using Equation (1) as long as ${\varrho }_m$ is greater than a given threshold ${\varrho }_{thr}$. This threshold on the cross-correlation coefficients was set at 0.99 to avoid OF measurement errors due to signals mismatching [26]. …”

“…A recent OF sensor was based on the M${}^2$APix (M${}^2$APix stands for Michaelis–Menten auto-adaptive pixel) retina that can auto-adapt in a seven-decade lighting range and responds appropriately to step changes up to ±3 decades [26]. The pixels do not saturate thanks to the (normalization based) intrinsic properties of the Michaelis–Menten equation [27].…”

A direct optic flow-based strategy for inverse flight altitude estimation with monocular vision and IMU measurements

“…In addition to this, several optical devices have been developed to facilitate the use of insect-inspired strategies in robots. Examples include a miniature artificial compound eye with a panoramic field of view [16], a robust minimalistic high frame rate optic flow sensor [17], an open hardware camera with a CMOS image sensor for optic flow estimates designed for MAVs [18], and a 33 mg 1D optic flow sensor for a flying microrobot [11].…”