Neural net based processor for robust, high-integrity multisensor and synthetic vision fusion

Kerr, J. R.; Luk, Chiu Hung; Hammerstrom, Dan; Pavel, Misha

doi:10.1109/dasc.2003.1245919

Cited by 4 publications

(1 citation statement)

References 12 publications

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“…Using vision to augment pilot aids is currently being pursued by several groups ͑Doehler, 2003; Kerr, Luk, Hammerstrom & Pavel, 2003͒. Unmanned aerial vehicles ͑UAVs͒ are also making use of vision.…”

Section: Introductionmentioning

confidence: 99%

Vision‐only control and guidance for aircraft

Proctor

Johnson

Apker

2006

Journal of Field Robotics

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An unmanned aerial vehicle usually carries an array of sensors whose output is used to estimate vehicle attitude, velocity, and position. This paper details the development of guidance, navigation, and control strategies for a glider, which is capable of flying a terminal trajectory to a known fixed object using only a single vision sensor. Controlling an aircraft using only vision presents two unique challenges: First, absolute state measurements are not available from a single image; and second, the images must be collected and processed at a high rate to achieve the desired controller performance. The image processor utilizes an integral image representation and a rejective cascade filter to find and classify simple features in the images, reducing the image to the most probable pixel location of the destination object. Then, an extended Kalman filter uses measurements obtained from a single image to estimate the states that would otherwise be unobservable in a single image. In this research, the flights are constrained to keep the destination object in view. The approach is validated through simulation. Finally, experimental data from autonomous flights of a glider, instrumented only with a single nose-mounted camera, intercepting a target window during short low-level flights, are presented.

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Section: Introductionmentioning

confidence: 99%

Vision‐only control and guidance for aircraft

Proctor

Johnson

Apker

2006

Journal of Field Robotics

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Biologically inspired enhanced vision system (EVS) for aircraft landing guidance

Luk

Gao

Hammerstrom

et al.

2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541)

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Vision-Based Hazard Detection

Luk¹,

Zaveri²,

Hammerstrom³

et al. 2006

Intelligent Engineering Systems Through Artificial Neural Networks, Volume 16

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Advances in sensor and GPS technologies make possible better guidance tools for vehicle/aerial navigation. In the work reported here we look at the detection of hazards, such as aircraft or other objects, on or near the target runway. Our system consists of two modules: 1) regions of interest (ROI) detection, and 2) hazard recognition. One of the harder problems in object recognition is to segment the target object from a cluttered background. In this system we use a "poor man's" segmentation, by taking advantage of the fact that we have an approximate reference that we can differentiate from. The regions of interest are defined as significant differences between the input image and the reference image. Since this differencing can be complex due to the fact that the images may not be precisely registered, we employed a novel histogram method which is reasonably invariant to spatial transformations for ROI detection.

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Neural net based processor for robust, high-integrity multisensor and synthetic vision fusion

Cited by 4 publications

References 12 publications

Vision‐only control and guidance for aircraft

Vision‐only control and guidance for aircraft

Biologically inspired enhanced vision system (EVS) for aircraft landing guidance

Vision-Based Hazard Detection

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