&lt;title&gt;Calculating time-to-collision with real-time optical flow&lt;/title&gt;

Camus, T.

doi:10.1117/12.186009

Cited by 10 publications

(10 citation statements)

References 5 publications

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“…The gannet is supposed to use this information when it quickly dives to catch a fish and determines the exact moment when to fold its wings before entering the water [23][24][25]. Camus [6] has implemented the corresponding strategy on a wheeled robot.…”

Section: Biological Inspirationmentioning

confidence: 99%

A contribution to vision-based autonomous helicopter flight in urban environments

Muratet

Doncieux

Brière³

et al. 2005

Robotics and Autonomous Systems

102

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A navigation strategy that exploits the optic flow and inertial information to continuously avoid collisions with both lateral and frontal obstacles has been used to control a simulated helicopter flying autonomously in a textured urban environment. Experimental results demonstrate that the corresponding controller generates cautious behavior, whereby the helicopter tends to stay in the middle of narrow corridors, while its forward velocity is automatically reduced when the obstacle density increases. When confronted with a frontal obstacle, the controller is also able to generate a tight U-turn that ensures the UAV's survival. The paper provides comparisons with related work, and discusses the applicability of the approach to real platforms.

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Section: Biological Inspirationmentioning

confidence: 99%

A contribution to vision-based autonomous helicopter flight in urban environments

Muratet

Doncieux

Brière³

et al. 2005

Robotics and Autonomous Systems

102

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“…This biologically inspired technique has been demonstrated indoors on small ground-based robots (Argyros, Tsakiris, & Groyer, 2004;Camus, 1994;Srinivasan et al, 1998) and on small fixedwing UAVs (Green, Oh, & Barrows, 2004;Zufferey, & Floreano, 2005). Our previous work (Hrabar & Sukhatme, 2004) was the first to investigate the use of optic flow for centering a rotorcraft UAV in an urban canyon.…”

Section: Related Workmentioning

confidence: 99%

“…This included generating yaw rate commands offline on data recorded onboard the UAV in an urban canyon but did not include closed-loop control. Optic flow has also been used to judge the time to collision with obstacles to the front (Camus, 1994) and to regulate a vehicle's speed when entering a cluttered environment and the height of a UAV above the ground (Garratt & Chahl, 2008). A drawback with optic flow-based techniques is that they do not provide absolute range measurements.…”

Section: Related Workmentioning

confidence: 99%

“…Optic flow can be used to estimate the time to collision with features to the front (Camus, 1994); however, this technique does not give absolute range measurements and is not effective at detecting narrow features. Stereo vision on the other hand does give absolute range measurements and has been successfully used for obstacle detection.…”

Section: Stereo Vision-based Obstacle Avoidancementioning

confidence: 99%

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Vision‐based navigation through urban canyons

Hrabar

Sukhatme

2009

Journal of Field Robotics

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We address the problem of navigating unmanned vehicles safely through urban canyons in two dimensions using only vision-based techniques. Two commonly used vision-based obstacle avoidance techniques (namely stereo vision and optic flow) are implemented on an aerial and a ground-based robotic platform and evaluated for urban canyon navigation. Optic flow is evaluated for its ability to produce a centering response between obstacles, and stereo vision is evaluated for detecting obstacles to the front. We also evaluate a combination of these two techniques, which allows a vehicle to detect obstacles to the front while remaining centered between obstacles to the side. Through experiments on an unmanned ground vehicle and in simulation, this combination is shown to be beneficial for navigating urban canyons, including T-junctions and 90-deg bends. Experiments on a rotorcraft unmanned aerial vehicle, which was constrained to two-dimensional flight, demonstrate that stereo vision allowed it to detect an obstacle to the front, and optic flow allowed it to turn away from obstacles to the side. We discuss the theory behind these techniques, our experience in implementing them on the robotic platforms, and their suitability to the urban canyon navigation problem. C 2009 Wiley Periodicals, Inc.

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“…Although flow can be used to judge the time-to-collision [4] with obstacles to the front, regulate the height flown above the ground and a vehicle's speed when entering a cluttered environment, its most common use is to produce a centering response when traveling between obstacles to the side [5] [6] [7] [8]. This centering response allows a UAV to fly down the center of an urban canyon for example, and is achieved by balancing the perceived flows on either side.…”

Section: Introductionmentioning

confidence: 99%

Optimum Camera Angle for Optic Flow-Based Centering Response

Hrabar

Sukhatme

2006

2006 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-We present analytical and empirical investigations into the optimum camera angle to use for the optic flow-based centering response. This technique is commonly used to guide both ground-based and aerial robots between obstacles. A variety of camera angles have been implemented by researchers in the past, but surprisingly little mention is made of the motivation for these camera angle choices, nor has an investigation into the optimum camera angle been conducted. Our investigation shows that camera angle plays a key role in the performance of control strategies for the centering response, and both empirical and analytical investigations show the optimum camera angle to be 45 degrees when traveling between parallel obstacles.

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<title>Calculating time-to-collision with real-time optical flow</title>

Cited by 10 publications

References 5 publications

A contribution to vision-based autonomous helicopter flight in urban environments

A contribution to vision-based autonomous helicopter flight in urban environments

Vision‐based navigation through urban canyons

Optimum Camera Angle for Optic Flow-Based Centering Response

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