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

Displacement Estimation Based on Optical and Inertial Sensor Fusion

This article aims to develop a system capable of estimating the displacement of a moving object with the usage of a relatively cheap and easy to apply sensors. There is a growing need for such systems, not only for robots, but also, for instance, pedestrian navigation. In this paper, the theory for this idea, including data postprocessing algorithms for a MEMS accelerometer and an optical flow sensor (OFS), as well as the developed complementary filter applied for sensor fusion, are presented. In addition, a vital part of the accelerometer’s algorithm, the zero velocity states detection, is implemented. It is based on analysis of the acceleration’s signal and further application of acceleration symmetrization, greatly improving the obtained displacement. A test stand with a linear guide and motor enabling imposing a specified linear motion is built. The results of both sensors’ testing suggest that the displacement estimated by each of them is highly correct. Fusion of the sensors’ data gives even better outcomes, especially in cases with external disturbance of OFS. The comparative evaluation of estimated linear displacements, in each case related to encoder data, confirms the algorithms’ operation correctness and proves the chosen sensors’ usefulness in the development of a linear displacement measuring system.

Towards Computational Models and Applications of Insect Visual Systems for Motion Perception: A Review

Motion perception is a critical capability determining a variety of aspects of insects' life, including avoiding predators, foraging, and so forth. A good number of motion detectors have been identified in the insects' visual pathways. Computational modeling of these motion detectors has not only been providing effective solutions to artificial intelligence, but also benefiting the understanding of complicated biological visual systems. These biological mechanisms through millions of years of evolutionary development will have formed solid modules for constructing dynamic vision systems for future intelligent machines. This article reviews the computational motion perception models originating from biological research on insects' visual systems in the literature. These motion perception models or neural networks consist of the looming-sensitive neuronal models of lobula giant movement detectors (LGMDs) in locusts, the translation-sensitive neural systems of direction-selective neurons (DSNs) in fruit flies, bees, and locusts, and the small-target motion detectors (STMDs) in dragonflies and hoverflies. We also review the applications of these models to robots and vehicles. Through these modeling studies, we summarize the methodologies that generate different direction and size selectivity in motion perception. Finally, we discuss multiple systems integration and hardware realization of these bio-inspired motion perception models.

Insect-inspired vision for autonomous vehicles

Flying insects are being studied these days as if they were agile micro air vehicles fitted with smart sensors, requiring very few brain resources. The findings obtained on these natural fliers have proved to be extremely valuable when it comes to designing compact low-weight artificial optical sensors capable of performing visual processing tasks robustly under various environmental conditions (light, clouds, contrast). Here, we review some outstanding bio-inspired visual sensors, which can be used for either detecting motion in the visible spectrum or controlling celestial navigation in the ultraviolet spectrum and for attitude stabilisation purposes. Biologically inspired visual sensors do not have to comprise a very large number of pixels: they are able to perform both short and long range navigation tasks surprisingly well with just a few pixels and a weak resolution.

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

With tiny and limited nervous systems, insects demonstrate a remarkable ability to fly through complex environments. Optic flow has been identified to play a crucial role in regulating flight conditions and navigation in flies and bees. In robotics, optic flow has been widely studied thanks to the low computational requirements. However, with only monocular visual information, optic flow is inherently devoid of a scale factor required for estimating the absolute distance. In this paper, we propose a strategy for estimating the flight altitude of a flying robot with a ventral camera by combining the optic flow with measurements from an inertial measurement unit. Instead of using the prevalent feature-based approach for calculation of optic flow, we implement a direct method that evaluates the flow information via image gradients. We show that the direct approach notably simplifies the computation steps compared to the feature-based method. When combined with an extended Kalman filter for fusion of inertial measurement units measurements, the flight altitude can be estimated in real time. We carried out extensive flight tests in different settings. Among 31 hovering and vertical flights near the altitude of 40 cm, we achieved the RMS errors in the altitude estimate of 2.51 cm. Further analysis of factors that affect the quality of the flow and the distance estimate is also provided.

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

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.