In this paper, we present: (i) a novel analog silicon retina featuring auto-adaptive pixels that obey the Michaelis-Menten law, i.e. V=V(m) I(n)/I(n)+σ(n); (ii) a method of characterizing silicon retinas, which makes it possible to accurately assess the pixels' response to transient luminous changes in a ±3-decade range, as well as changes in the initial steady-state intensity in a 7-decade range. The novel pixel, called M(2)APix, which stands for Michaelis-Menten Auto-Adaptive Pixel, can auto-adapt in a 7-decade range and responds appropriately to step changes up to ±3 decades in size without causing any saturation of the Very Large Scale Integration (VLSI) transistors. Thanks to the intrinsic properties of the Michaelis-Menten equation, the pixel output always remains within a constant limited voltage range. The range of the Analog to Digital Converter (ADC) was therefore adjusted so as to obtain a Least Significant Bit (LSB) voltage of 2.35mV and an effective resolution of about 9 bits. The results presented here show that the M(2)APix produced a quasi-linear contrast response once it had adapted to the average luminosity. Differently to what occurs in its biological counterparts, neither the sensitivity to changes in light nor the contrast response of the M(2)APix depend on the mean luminosity (i.e. the ambient lighting conditions). Lastly, a full comparison between the M(2)APix and the Delbrück auto-adaptive pixel is provided.

For use in autonomous micro air vehicles, visual sensors must not only be small, lightweight and insensitive to light variations; on-board autopilots also require fast and accurate optical flow measurements over a wide range of speeds. Using an auto-adaptive bio-inspired Michaelis–Menten Auto-adaptive Pixel (M2APix) analog silicon retina, in this article, we present comparative tests of two optical flow calculation algorithms operating under lighting conditions from 6×10−7 to 1.6×10−2 W·cm−2 (i.e., from 0.2 to 12,000 lux for human vision). Contrast “time of travel” between two adjacent light-sensitive pixels was determined by thresholding and by cross-correlating the two pixels’ signals, with measurement frequency up to 5 kHz for the 10 local motion sensors of the M2APix sensor. While both algorithms adequately measured optical flow between 25 ∘/s and 1000 ∘/s, thresholding gave rise to a lower precision, especially due to a larger number of outliers at higher speeds. Compared to thresholding, cross-correlation also allowed for a higher rate of optical flow output (99 Hz and 1195 Hz, respectively) but required substantially more computational resources.

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.

No abstract

In an unknown environment, assessing the robot trajectory in real time is one of the key issues for a successful robotic mission. In such environment, the absolute measurements like the GPS data may be unavailable. Moreover, estimating the position using only proprioceptive sensors like encoders and Inertial Measurements Units (IMU) will generate errors that increase over time. This paper presents a multi-sensor fusion approach between IMU and ground Optical Flow (OF) used to estimate the position of a mobile robot while ensuring high integrity localization. The data fusion in done through the informational form of the Kalman Filter namely Information Filter (IF). A Fault Detection and Exclusion (FDE) step is added in order to exclude the erroneous measurements from the fusion procedure by making it fault tolerant and to ensure a high localization performance. The approach is based on the use of the IF for the state estimation and tools from the information theory for the FDE. Our proposed approach evaluates the quality of a measurement based on the amount of information it provides using informational metrics like the Kullback-Leibler divergence. The approach is validated on data obtained from experiments performed in outdoor environments in various conditions including high-dynamic-range lighting and different ground textures.

In this paper, we present (i) a novel bio-inspired 1-D OF sensor which is robust to high-dynamic-range lighting conditions and independent of the visual patterns encountered, and (ii) a low-cost car-like robot called BioCarBot, which estimates its velocity and steering angle by means of an Extended Kalman Filer (EKF) using only the OF measurements delivered by two downward-facing sensors of this kind. Indoor experiments were carried out, in which the robot was driven in the closed-loop mode, using a proportional integral (PI) controller based on the velocity and steering angle estimates. The results presented here show that our novel OF sensor can deliver a wide range of high-frequency (333 Hz) OF measurements (from 1 to 10 rad s ) with a relatively high resolution (up to 0.05 rad s ) in a 5-decade high-dynamic range of light levels. Neither the refresh rate nor the resolution of the OF sensors presented here depended on either the visual patterns or the lighting conditions, and could be theoretically set at whatever value required.

Abstract-Although several (semi-) automatic parking systems have been presented throughout the years [1]- [12], car manufacturers are still looking for low-cost sensors providing redundant information about the obstacles around the vehicle, as well as efficient methods of processing this information, in the hope of achieving a very high level of robustness. We therefore investigated how Local Motion Sensors (LMSs) [13], [14], comprising only of a few pixels giving 1-D optical flow (OF) measurements could be used to improve automatic parking maneuvers. For this purpose, we developed a low computationalcost method of detecting and tracking a parking spot in real time using 1-D OF measurements around the vehicle as well as the vehicle's longitudinal velocity and steering angle. The algorithm used was composed of 5 processing steps, which will be described here in detail. In this initial report, we will first present some results obtained in a highly simplified 2-D parking simulation performed using Matlab/Simulink software, before giving some preliminary experimental results obtained with the first step in the algorithm in the case of a vehicle equipped with two 6-pixel LMSs. The results of the closed-loop simulation show that up to a certain noise level, the simulated vehicle detected and tracked the parking-spot assessment in real time. The preliminary experimental results show that the average refresh frequency obtained with the LMSs was about 2-3 times higher than that obtained with standard ultrasonic sensors and cameras, and that these LMSs therefore constitute a promising alternative basis for designing new automatic parking systems.