&lt;title&gt;Real-time optical-flow range estimation on the iWarp&lt;/title&gt;

Fong, Terrence; Suorsa, Raymond E.

doi:10.1117/12.141770

SPIE Proceedings

1993

DOI: 10.1117/12.141770

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<title>Real-time optical-flow range estimation on the iWarp</title>

Terrence Fong¹,

Raymond E. Suorsa

Abstract: This paper describes the implementation of an optical ow range-estimation algorithm, intended to support autonomous helicopter navigation, on the iWarp distributed-memory multicomputer. The implementation exploits numerous features of the iWarp communications and computation architecture to maximize performance and e ciency. The range-estimation algorithm has been previously implemented on both distributed and shared-memory workstations. Experiences and performance gures from the initial iWarp implementation a… Show more

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A parallel implementation of a multisensor feature-based range-estimation method

Suorsa

Sridhar

1994

IEEE Trans. Robot. Automat.

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There are many proposed vision based methods to perform obstacle detection and avoidance for autonomous or semi-autonomous vehicles. All methods, however, will require very high processing rates to achieve real time performance. A system capable of supporting autonomous helicopter navigation will need to extract obstacle information from imagery at rates wrying from ten frames per second to thirty or more frames per second depending on the vehicle speed. Such a system will need to sustain billions of operations per second. To reach such high processing rates using current technology, a parallel implementation of the obstacle detection/ranging method is required. This paper describes an efficient and flexible parallel implementation of a multisensor feature-based range-estimation algorithm, targeted for helicopter flight, realized on both a distributed-memory and shared-memory parallel computer. 17. SECURITY CLASSIFICATION 18. SECURITY CLASSIFICATION OF REPORT OF THIS PAGE

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A parallel implementation of a multisensor feature-based range-estimation method

Suorsa

Sridhar

1994

IEEE Trans. Robot. Automat.

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Results from the NASA Automated Nap-of-the-Earth Program

Zelenka¹,

Smith²,

Coppenbarger³

et al. 1997

j am helicopter soc

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Military helicopter Nap-of-the-Earth (NOE) flight is one of the most demanding low-altitude, near terrain Nght operations.In NOE, the pilot is operating a t o r below tree-top levels, taking maximum advantage of the terrain and ground featurn for concealment Such increased proximity to obstructions places heightened maneuverability rquirements on the aircrafl and extreme levels of workload on the pilotThe basic issue being addressed in the NASA Automated Nap-of-the-Earth (ANOE) program is the intelligent use of environmental information such as knowledge of terrain, obstacles, and other external factors to enhance the flight path guidance of the vehicle. This is a major departure over contemporary guidanceand control based on feedback of state variablessuch as vehicle attitudes, velocities, and accelerations. Although the immediate program has a military foeus, the technological advances inherent for automating NOE flight have great benefit to the operation of a wide class of vehicles such as emergency medical helicopters, conventional and high-speed transports, unmanned aerial vehicles, and planetary vehicles.This paper summarizes results to date of the NASA ANOE program in the areas of passive and active sensors, pilot dii plays, low-altitude manual trajectory guidance, and NOE automatic guidance. Each of these areas has been developed and evaluated in piloted, motion-bawd simulation o r through flight test These evaluations have demonstrated the feasibility of automating the NOE flight &ion, and have generated additional spin-off applications of the technologies.

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<title>Real-time optical-flow range estimation on the iWarp</title>

Cited by 2 publications

References 2 publications

A parallel implementation of a multisensor feature-based range-estimation method

A parallel implementation of a multisensor feature-based range-estimation method

Results from the NASA Automated Nap-of-the-Earth Program

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