A terahertz frequency-based sensing system for an agricultural harvester is provided. The system includes a terahertz sensor mounted to the agricultural harvester. The terahertz sensor at least one a terahertz source disposed to direct electromagnetic radiation toward a harvest material of the agricultural harvester. At least one terahertz detector is disposed to detect the terahertz electromagnetic radiation after the terahertz electromagnetic radiation interacts with the harvest material. A controller is operably coupled to the at least one terahertz detector and is configured to detect at least one harvest-related parameter based on a signal from the at least one terahertz detector and to perform an action based on the at least one detected parameter.

A device configured to capture a first image of an item on a platform using a camera and to determine a first number of pixels in the first image that corresponds with the item. The device is further configured to capture a first depth image of an item on the platform using a three-dimensional (3D) sensor and to determine a second number of pixels within the first depth image that corresponds with the item. The device is further configured to determine that the difference between the first number of pixels in the first image and the second number of pixels in the first depth image is less than the difference threshold value, to extract the plurality of pixels corresponding with the item in the first image from the first image to generate a second image, and to output the second image.

A method for classifying an object in a point cloud includes computing first and second classification statistics for one or more points in the point cloud. Closest matches are determined between the first and second classification statistics and a respective one of a set of first and second classification statistics corresponding to a set of N classes of a respective first and second classifier, to estimate the object is in a respective first and second class. If the first class does not correspond to the second class, a closest fit is performed between the point cloud and model point clouds for only the first and second classes of a third classifier. The object is assigned to the first or second class, based on the closest fit within near real time of receiving the 3D point cloud. A device is operated based on the assigned object class.

A method for recognizing an object located in an object space includes emitting a distance measuring pulse into the object space by a signal time-of-flight based distance measuring unit. The object is provided with a marker which, in response to the influence of the distance measuring pulse, emits electromagnetic marker radiation in which object information for the object recognition is stored. The method further includes recording the marker radiation by an electrical radiation detector and the object information for object recognition being assigned to the object.

A data conversion processing unit converts a second group including a plurality of reflection point data units in which a reflection point corresponding to each reflection point data unit belongs to a three-dimensional object among a first data unit group into a third group including a plurality of projection point data units by projecting the second group onto a horizontal plane in a world coordinate system. A clustering processing unit clusters the plurality of projection point data units of the third group into a plurality of clusters based on positions of these units on the horizontal plane. A space of interest setting unit sets a space of interest for each cluster by using the plurality of reflection point data units corresponding to the plurality of projection point data units included in each cluster.

In step S11, a color image CIMG is acquired. In step S12, a distance image DIMG is acquired. In step S13, the color image CIMG is projected onto the distance image DIMG. An alignment of the color image CIMG and the distance image DIMG is performed prior to the projection of the color image CIMG. In step S14, it is determined whether or not a basic condition is satisfied. In S15, it is determined whether or not a special condition is satisfied. If a judgement result of the steps S14 or S15 is positive, then in step S16 a first data point and a second data point on the distance image DIMG are associated. If bot of the judgement results of steps S14 and S15 are negative, data points are not associated in step S17.

Jitter is removed from point cloud data of a target by fitting the data to 3-D models of possible targets. The point cloud data is de-jittered as a group by shifting the point cloud data in its coordinate system until a minimum fit error is observed between the shifted data and a 3-D model under analysis. Different 3-D models may be evaluated in succession until a 3-D model is identified that has the least fit error. The 3-D model with the least fit error most likely represents the identity of the target.

A distance measurement apparatus includes a light emitting apparatus capable of emitting first light and second light having a smaller spread than the first light, and changing an emission direction of the second light, a light receiving apparatus, and a processing circuit. The processing circuit performs a process including causing the light receiving apparatus to detect reflected light that occurs due to the first light and reflected light that occurs due to the second light, and generate therefrom first distance data and second distance data, when an object is present outside a first target area included in an area illuminated by the first light, causing the light emitting apparatus to track the object by the second light; and when the object enters the inside of the first target area from the outside of the first target area, causing the light emitting apparatus to stop the tracking by the second light.

A method of detecting a road-curb that is performed by a road-curb detecting apparatus is provided. The method includes obtaining points around a lidar sensor from the lidar sensor, arranging the points in a plurality of cells into which a circular grid map is divided, and detecting the road-curb based on the points arranged in the plurality of cells.

A detection system includes an acquisition portion scanning light to acquire point-cloud information corresponding to a plurality of positions of a detection target object; an estimation portion using consistency with an outer shape model of the detection target object to estimate a location and attitude of the detection target object based on the point-cloud information; and an output portion outputting information relating to a movement target location based on an estimation result, wherein the estimation portion fits an outer shape model indicating an outer shape of the detection target object to a point cloud according to the point-cloud information, and uses point-cloud information existing outside the outer shape model to estimate the location and the attitude of the detection target object.