Abstract: A method, system and robot, wherein the robot includes two or more sensing devices, sensor A and sensor B, mounted thereon and moves forward along an axis parallel to the rows of plants, being autonomously steered by exerting angular corrections to place the robot as close as possible to the centerline between the rows of plants, wherein the method and system includes the following: (ii) dividing a two-dimensional grid of square cells into groups of cells; (iii) obtaining data points using sensor A and sensor B; (vii) moving the robot: (a) by turning right; or (b) by turning left; or (c) forward without turning, depending on whether each group of cells is calculated as low-activated, high-activated or not activated using the data points.

Abstract: A method, system and robot for autonomous navigation thereof between two rows of plants, wherein said robot includes two or more sensing devices, sensor A and sensor B, mounted thereon and moves forward along an axis parallel to the rows of plants, being autonomously steered by exerting angular corrections to place the robot as close as possible to the centerline between the rows of plants, wherein the method and system includes the following: (ii) dividing a two-dimensional grid of square cells into IG·JG groups of cells; (iii) obtaining data points using sensor A and sensor B; (vii) moving the robot: (a) by turning right; or (b) by turning left; or (c) forward without turning, depending on whether each group of cells (i,j) is calculated as low-activated, high-activated or not activated using said data points.

Abstract: A system and method for creating 3-dimensional agricultural field scene maps are disclosed comprising producing a pair of images using a stereo camera and creating a disparity image based on the pair of images, the disparity image being a 3-dimensional representation of the stereo images. Coordinate arrays can be produced from the disparity image and the coordinate arrays can be used to render a 3-dimensional local map of the agricultural field scene. Global maps can also be made by using geographic location information associated with various local maps to fuse together multiple local maps into a 3-dimensional global representation of the field scene. The disparity images can be used to analyze a variety of agricultural features.

Abstract: A system and method for creating 3-dimensional agricultural field scene maps are disclosed comprising producing a pair of images using a stereo camera and creating a disparity images based on the pair of images, the disparity image being a 3-dimensional representation of the stereo images. Coordinate arrays can be produced from the disparity image and the coordinate arrays can be used to render a 3-dimensional local map of the agricultural field scene. Global maps can also be made by using geographic location information associated with various local maps to fuse together multiple local maps into a 3-dimensional global representation of the field scene. The disparity images can be used to analyze a variety of agricultural features.

Abstract: A system and method for creating 3-dimensional agricultural field scene maps are disclosed comprising producing a pair of images using a stereo camera and creating a disparity images based on the pair of images, the disparity image being a 3-dimensional representation of the stereo images. Coordinate arrays can be produced from the disparity image and the coordinate arrays can be used to render a 3-dimensional local map of the agricultural field scene. Global maps can also be made by using geographic location information associated with various local maps to fuse together multiple local maps into a 3-dimensional global representation of the field scene.

Abstract: A discriminator identifies windrow pixels associated with a windrow within a collected image. A definer defines a search space with respect to a vehicle. An evaluator determines respective spatial correlations between the defined search space and the windrow pixels for different angular displacements of the search space. An alignment detector or search engine determining a desired vehicular heading as a preferential angular displacement associated with a generally maximum spatial correlation between the defined search space and the windrow pixels. An offset calculator estimates an offset of the vehicle to a central point of the windrow or a depth axis to achieve the desired vehicle heading and desired position of the vehicle with respect to the windrow.

Abstract: A discriminator identifies windrow pixels associated with a windrow within a collected image. A definer defines a search space with respect to a vehicle. An evaluator determines respective spatial correlations between the defined search space and the windrow pixels for different angular displacements of the search space. An alignment detector or search engine determining a desired vehicular heading as a preferential angular displacement associated with a generally maximum spatial correlation between the defined search space and the windrow pixels. An offset calculator estimates an offset of the vehicle to a central point of the windrow or a depth axis to achieve the desired vehicle heading and desired position of the vehicle with respect to the windrow.

Abstract: A discriminator identifies windrow pixels associated with a windrow within a collected image. A definer defines a search space with respect to a vehicle. An evaluator determines respective spatial correlations between the defined search space and the windrow pixels for different angular displacements of the search space. An alignment detector or search engine determining a desired vehicular heading as a preferential angular displacement associated with a generally maximum spatial correlation between the defined search space and the windrow pixels. An offset calculator estimates an offset of the vehicle to a central point of the windrow or a depth axis to achieve the desired vehicle heading and desired position of the vehicle with respect to the windrow.

Abstract: A method and system for guiding a vehicle comprises a location determining receiver for collecting location data for the vehicle. A vision module collects vision data for the vehicle. A location quality estimator estimates the location quality data for the location data during an evaluation time window. A vision module estimates vision quality data for the vision data during the evaluation time window. A supervisor module selects a mixing ratio for the vision data and location data (or error signals associated therewith) based on the quality data.

Abstract: A method and system for guiding a vehicle comprises a location determining receiver for collecting location data for the vehicle. A vision module collects vision data for the vehicle. A location quality estimator estimates the location quality data for the location data during an evaluation time window. A vision module estimates vision quality data for the vision data during the evaluation time window. A supervisor module selects at least one of a location data weight and a vision data weight based on the quality data.

Abstract: An image collection system collects stereo image data comprising left image data and right image data of a particular scene. An estimator estimates a reference three dimensional image density rating for corresponding reference volumetric cell within the particular scene at a reference range from an imaging unit. An image density compensator compensates for the change in image density rating between the reference volumetric cell and an evaluated volumetric cell by using a compensated three dimensional image density rating based on a range or relative displacement between the reference volumetric cell and the evaluated volumetric cell.

Abstract: An image collection system collects stereo image data comprising left image data and right image data of a particular scene. An estimator estimates a reference three dimensional image density rating for corresponding reference volumetric cell within the particular scene at a reference range from an imaging unit. An image density compensator compensates for the change in image density rating between the reference volumetric cell and an evaluated volumetric cell by using a compensated three dimensional image density rating based on a range or relative displacement between the reference volumetric cell and the evaluated volumetric cell.

Abstract: The entire right, title and interest in and to this application and all subject matter disclosed and/or claimed therein, including any and all divisions, continuations, reissues, etc., thereof are, effective as of the date of execution of this application, assigned, transferred, sold and set over by the applicant(s) named herein to Deere & Company, a Delaware corporation having offices at Moline, Ill. 61265, U.S.A., together with all rights to file, and to claim priorities in connection with, corresponding patent applications in any and all foreign countries in the name of Deere & Company or otherwise.

Abstract: A method and system for guiding a vehicle comprises a location determining receiver for collecting location data for the vehicle. A vision module collects vision data for the vehicle. A location quality estimator estimates the location quality data for the location data during an evaluation time window. A vision module estimates vision quality data for the vision data during the evaluation time window. A supervisor module selects at least one of a location data weight and a vision data weight based on the quality data.

Abstract: A method and system for guiding a vehicle comprises a location determining receiver for collecting location data for the vehicle. A vision module collects vision data for the vehicle. A location quality estimator estimates the location quality data for the location data during an evaluation time window. A vision module estimates vision quality data for the vision data during the evaluation time window. A supervisor module selects a mixing ratio for the vision data and location data (or error signals associated therewith) based on the quality data.

Abstract: A system and method for creating 3-dimensional agricultural field scene maps are disclosed comprising producing a pair of images using a stereo camera and creating a disparity images based on the pair of images, the disparity image being a 3-dimensional representation of the stereo images. Coordinate arrays can be produced from the disparity image and the coordinate arrays can be used to render a 3-dimensional local map of the agricultural field scene. Global maps can also be made by using geographic location information associated with various local maps to fuse together multiple local maps into a 3-dimensional global representation of the field scene.

Abstract: A system and method for creating 3-dimensional agricultural field scene maps are disclosed comprising producing a pair of images using a stereo camera and creating a disparity image based on the pair of images, the disparity image being a 3-dimensional representation of the stereo images. Coordinate arrays can be produced from the disparity image and the coordinate arrays can be used to render a 3-dimensional local map of the agricultural field scene. Global maps can also be made by using geographic location information associated with various local maps to fuse together multiple local maps into a 3-dimensional global representation of the field scene. The disparity images can be used to analyze a variety of agricultural features.

Abstract: A system and method for creating 3-dimensional agricultural field scene maps are disclosed comprising producing a pair of images using a stereo camera and creating a disparity images based on the pair of images, the disparity image being a 3-dimensional representation of the stereo images. Coordinate arrays can be produced from the disparity image and the coordinate arrays can be used to render a 3-dimensional local map of the agricultural field scene. Global maps can also be made by using geographic location information associated with various local maps to fuse together multiple local maps into a 3-dimensional global representation of the field scene. The disparity images can be used to analyze a variety of agricultural features.