Optic flow

Koenderink, Jan J.

doi:10.1016/0042-6989(86)90078-7

Cited by 729 publications

(452 citation statements)

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“…To safely fly in cluttered environments, insects instead rely on image motion, also known as optic flow 38,39 , generated by their own displacement relative to the surroundings 40 . It has been experimentally shown that their neural system reacts to optic flow patterns 41,42 to produce a large variety of flight capabilities, such as obstacle avoidance 40,43 , speed maintenance 44 , odometry estimation 45 , wall following and corridor centring 46 , altitude regulation 47,48 , orientation control 49 and landing 50,51 .…”

Section: Review Insightmentioning

confidence: 99%

Science, technology and the future of small autonomous drones

Floreano

Wood

2015

Nature

1,050

552

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Drones, a popular nickname for unmanned aerial vehicles and micro aerial vehicles, often conjure up images of unmanned aeroplanes that fly thousands of miles for espionage and to deploy munitions. However, over the past few years, an increasing number of public and private research laboratories have been working on small, human-friendly drones that one day may autonomously fly in confined spaces and in close proximity to people. The development of these small drones, which is the main focus of this Review, has been supported by the miniaturization and cost reduction of electronic components (microprocessors, sensors, batteries and wireless communication units), largely driven by the portable electronic device industry. These improvements have enabled the prototyping and commercialization of small (typically less than 1 kg) drones at smartphone prices.Small drones will have important socio-economic impacts (Fig. 1). Images from drones that are capable of flying a few metres above the ground will fill a gap between expensive, weather-dependent and lowresolution images provided by satellites and car-based images limited to human-level perspectives and the availability of accessible roads. Specialized flying cameras and cloud-based data analytics will allow farmers to continuously monitor the quality of crop growth. Such platforms will enable construction companies to measure work progress in real time. Drones will let mining companies obtain precise volumetric data of excavations. Energy and infrastructure companies will be able to exhaustively survey pipelines, roads and cables. Humanitarian organizations could immediately assess and adapt aid efforts in continuously changing refugee camps. Transportation drones that are capable of safely taking off and landing in the proximity of buildings and humans will allow developing countries -without a suitable road network -to rapidly deliver goods and to finally unleash the full potential of their e-commerce telecommunication infrastructure. Transportation drones will also help developed countries to improve the quality of service in congested or remote areas, and will enable rescue organizations to quickly deliver medical supplies in the field and on demand. Inspection drones that are capable of flying in confined spaces will help fire-fighting and emergency units to assess dangers faster and more safely, logistic companies to detect cracks in the inner and outer shells of ships, road maintenance companies to measure signs of wear and tear in bridges and tunnels, security companies to improve building safety by monitoring areas outside the range of surveillance cameras, and disaster mitigation agencies to inspect partially collapsed buildings where ground clutter is an obstacle for terrestrial robots. Coordinated teams of autonomous drones will enable missions that last longer than the flight time of a single drone by allowing some drones to temporarily leave the team for battery replacement. Drone teams will permit rescue organizations to quickly deploy dedicated commu...

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Section: Review Insightmentioning

confidence: 99%

Science, technology and the future of small autonomous drones

Floreano

Wood

2015

Nature

1,050

552

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A cortical area that responds specifically to optic flow, revealed by fMRI

et al. 2000

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articlesThe optic flow 1 that is generated when a person moves through the environment can be locally decomposed into several basic components, including radial, circular, translation and sheer motion 2,3 . Neurons in the dorsal portion of the medial superior temporal cortex (MSTd) of macaque monkeys respond selectively to these components, alone or in combination [4][5][6][7] . Microstimulation can influence the direction of heading of a behaving monkey 8 , which demonstrates the functional importance of MST to heading. In the adjacent area MT (or V5), neurons also respond to motion and are highly directionally selective, but they do not show specific selectivity to circular or radial trajectories 9 . The neurophysiology findings are reinforced by psychophysical studies suggesting the existence of analogous neural units in humans that integrate local-motion signals along complex flow trajectories 10,11 . In agreement with the neurophysiological studies, these units have very large receptive fields 12 and sum information over periods of one or two seconds (L. Santoro and D.C. Burr, Perception 28, 90c, 1999). There is also some evidence in humans for selectivity along the 'cardinal directions' of optic flow (radial and circular) 13 , although this issue is somewhat controversial 14 .A very strong motion-selective response at the boundary of Brodmann's areas 19 and 37 is shown by human imaging [15][16][17][18][19][20][21][22][23] . This area is generally thought to be the human analogue of monkey V5/MT-MST regions, and is referred to as 'V5/MT complex' (MT+). Other motion-sensitive areas have been identified: a dorsal region referred to as V3A 16,21,22,24 and a ventral region, seemingly activated by motion boundaries and second-order motion 20,21 . Here, using fMRI, we examined the response to optic-flow stimuli, and showed selective activation of a large area within the V5/MT complex to radial and circular motion, very different from the area activated by translational motion. Activation in response to optic flow occurred only when the direction of optic flow changed-abruptly or gradually-during the presentation period; continuous radial or circular motion produced no reliable activation in any area when measured against a matched random control. RESULTS Response to optic flowWe created dynamic displays of circular, radial, spiral and translational motion from random-dot patterns. The control stimuli were locally identical to the active stimuli; each dot moved along independent, randomly chosen trajectories ( Fig. 1; Methods). Responses of subjects to flow motion were measured against these random controls, with both active and control stimuli reversing direction every two seconds. There was a clear and specific activation in the temporal-occipital cortex, with no response in V1 or any other cortical area (example, Fig. 2a). The response extended for more than 1 cm along the sulcus that separates Brodmann's area 19 and 37, within the region that is usually referred to the human analogue of V5/MT complex [15][16][...

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“…Several algorithms based on the rigidity assumption for special cases, such as planes, have been developed (Mayhew and Longuet-Higgins 1982). Psychophysical results show that human performance on some sfm tasks is at least broadly consistent with predictions based on the rigidity assumption (Wallach and O'Connell 1953;Koenderik 1986). More recent affine models are based only on local velocity information, rather than on the entire optic flow field, to account for human perception (Todd and Bressan 1990;Todd and Norman 1991).…”

Section: Introductionmentioning

confidence: 80%

A unified probabilistic model of the perception of three-dimensional structure from optic flow

Colas

et al. 2007

Biol Cybern

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To cite this version:Francis Colas, Jacques Droulez, Mark Wexler, Pierre Bessiere. A unified probabilistic model of the perception of three-dimensionnal structure from optic flow. Human observers can perceive the threedimensional (3-D) structure of their environment using vari.. 2006. The date of receipt and acceptance will be inserted by the editor Abstract Human observers can perceive the threedimensional (3-D) structure of their environment using various cues, an important one of which is motion parallax. The motion of any point's projection on the retina depends both on the point's movement in space and on its distance from the eye. Therefore, retinal motion can be used to extract the 3-D structure of the environment and the shape of objects, in a process known as structurefrom-motion (sfm). However, because many combinations of 3-D structure and motion can lead to the same optic flow, sfm is an ill-posed inverse problem. The rigidity assumption is a constraint supposed to formally solve the sfm problem and to account for human performance. Recently, however, a number of psychophysical results, in both moving and stationary human observers, have shown that the rigidity assumption alone cannot account for sfm, but no model is known to account for the new results. Here, we construct a Bayesian model of sfm based on only one new assumption, that of stationarity, coupled with the assumption of rigidity. The predictions of the model, calculated using a new and powerful methodology called Bayesian programming, account for a wide variety of experimental findings.

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Optic flow

Cited by 729 publications

References 30 publications

Science, technology and the future of small autonomous drones

Science, technology and the future of small autonomous drones

A cortical area that responds specifically to optic flow, revealed by fMRI

A unified probabilistic model of the perception of three-dimensional structure from optic flow

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