Real-Time Visual Sensing for Task Planning in a Field Navigation Vehicle

Sanchiz, José M.; Marchant, J.A.; Pla, Filiberto; Hague, T.

doi:10.1006/rtim.1996.0063

Cited by 6 publications

(2 citation statements)

References 12 publications

(9 reference statements)

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“…Images are segmented [9] to divide the scene into three classes, regions of class "soil", "plant" and "weed". The same images are used to identify the plants and to compute the motion parameters, which are used to place the images on a map of the field, built up while the vehicle moves [10]. Exploring the map along the nozzle bar allows us to open those nozzles that are over a plant or weed.…”

Section: Resultsmentioning

confidence: 99%

A framework for feature-based motion recovery in ground plane vehicle navigation

Sanchiz

Pla²,

Marchant

1997

Computer Analysis of Images and Patterns

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This paper describes a feature point matching strategy and motion recovery applied to vehicle navigation. A transformation of the image plane is used that keeps the motion of the vehicle parallel to the transformed plane. These allows us to define linear tTacking filters to estimate the real-world positions of the features. The correspondences between features are first selected by similarity, taking into account the smoothness of motion and rigidity of the scene. Further processing brings out the rest of correspondences. The method is applied to a real application consisting of an autonomous vehicle navigating in a crop field.

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Section: Resultsmentioning

confidence: 99%

A framework for feature-based motion recovery in ground plane vehicle navigation

Sanchiz

Pla²,

Marchant

1997

Computer Analysis of Images and Patterns

Self Cite

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“…Machine vision has been widely used in field automated guidance system, such as harvesting (Benson et al, 2003;Debain et al, 2000;Ollis and Stentz, 1996), cultivating (Hague and Tillett,2001;Olsen, 1995;Rovira Más, 2005), and spraying (Sanchiz et al, 1998).…”

Section: Introductionmentioning

confidence: 99%

Stereovision-based 3D field recognition for automatic guidance system of off-road vehicle

et al. 2005

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A stereovision-based disparity evaluation algorithm was developed for rice crop field recognition. The gray level intensities and the correlation relation were integrated to produce the disparities of stereo-images. The surface of ground and rice were though as two rough planes, but their disparities waved in a narrow range. The cut/uncut edges of rice crops were first detected and track through the images. We used a step model to locate those edge positions. The points besides the edges were matched respectively to get disparity values using area correlation method. The 3D camera coordinates were computed based on those disparities. The vehicle coordinates were obtained by multiplying the 3D camera coordinates with a transform formula. It has been implemented on an agricultural robot and evaluated in rice crop field with straight rows. The results indicated that the developed stereovision navigation system is capable of reconstructing the field image.

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Feature correspondence and motion recovery in vehicle planar navigation

Sanchiz

Pla

1999

Pattern Recognition

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Real-Time Visual Sensing for Task Planning in a Field Navigation Vehicle

Cited by 6 publications

References 12 publications

A framework for feature-based motion recovery in ground plane vehicle navigation

A framework for feature-based motion recovery in ground plane vehicle navigation

Stereovision-based 3D field recognition for automatic guidance system of off-road vehicle

Feature correspondence and motion recovery in vehicle planar navigation

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