An agricultural method includes providing a positive air pressure chamber to prevent outside contaminants from entering the chamber; growing crops in a plurality of cells in the chamber, each cell having multi-grow benches or levels, each cell further having connectors to vertical hoists for vertical movements in the chamber; maintaining pre-set temperature, humidity, carbon dioxide, watering and lighting levels to achieve predetermined plant growth; using motorized transport rails to deliver benches for operations including seeding, harvesting, grow media recovery, and bench wash; dispensing seeds in the cell with a mechanical seeder coupled to the transport rails; growing the crops with computer controlled nutrients, light and air level; and harvesting the crops and delivering the harvested crop at a selected outlet of the chamber.

To provide an own position estimation apparatus and an own position estimation method which can correct the position coordinate of own vehicle, even if a periphery monitoring apparatus in which detection points detected with good accuracy at the same timing is few is used. An own position estimation apparatus detects relative positions of a road side wall based on detection information of a periphery monitoring apparatus; converts the past relative positions, into relative positions on a basis of the current position of the own vehicle, and superimposes the current relative positions and the past relative positions after conversion; searches for a relative position relation that a coincidence degree between the relative positions of the road side wall after superposition and the positions of the road side wall of the map data becomes high; and corrects the position coordinate of the own vehicle based on the relative position relation.

A vehicular vision system includes a camera disposed at a vehicle and capturing image data. The vehicular vision system, via processing at an electronic control unit of a first frame of image data captured by the camera, detects a first object exterior of the vehicle and determines an attribute of the first object. The vehicular vision system, via processing at the electronic control unit of a second frame of image data captured by the camera, detects a second object exterior of the vehicle and determines the attribute of the second object. The system determines whether the first object and the second object are the same object based on a similarity measurement. The vehicular vision system, responsive to determining that the first object and the second object are the same object, merges the attribute of the first object with the attribute of the second object.

A problem of the present invention is to provide an object detection device etc. that can accurately detect an object regardless of a view angle position of and distance to the object. An object detection device of the present invention has: a stereo distance detection portion 105 that detects a distance to an object; a position detection portion 106 that detects a position of the object; a pose detection portion 111 that detects a pose of the object; a vehicle information input portion that inputs state information about a host vehicle and a different vehicle; a position prediction portion 109 that predicts a position of the different vehicle based on the state information about the host vehicle and the different vehicle; a pose prediction portion 110 that predicts a pose of the different vehicle based on the state information about the host vehicle and the different vehicle; and a determination portion 112 that determines a distance to, a position of, and a pose of the different vehicle in response to the information detected or predicted by the distance detection portion, the position detection portion, the pose detection portion, the position prediction portion, and the pose prediction portion.

An obstacle tracking method, a storage medium, and an electronic device are provided. In various embodiments for obstacles in every two frames of laser point clouds, first, the obstacles in the two frames of laser point clouds are matched according to types of the obstacles in the two frames of laser point clouds. Next, unmatched obstacles in the two frames of laser point clouds are matched according to point cloud data of the unmatched obstacles in the two frames of laser point clouds. After two times of matching, motion states of the obstacles in the two frames of laser point clouds are updated.

Systems and methods are provided for augmenting upright object detection. In one implementation, a system for augmenting detection of objects in an environment of a vehicle may include at least one processing device. The at least one processing device may be programmed to: receive, from an image capture device, a first image frame, the first image frame including an attention area associated with a suspected upright object indication; warp, using a level road plane model, an area in a second earlier image frame that corresponds to the attention area; track a plurality of image patches across the warped area and the attention area; compute a road plane model fit to the tracked image patches; and determine whether to suppress the upright object indication based on the tracked image patches being more consistent with a road plane model than with an upright object model.

A computer vision system is provided that includes an image generation device configured to generate consecutive two dimensional (2D) images of a scene, and a dense optical flow engine (DOFE) configured to determine a dense optical flow map for pairs of the consecutive 2D images, wherein, for a pair of consecutive 2D images, the DOFE is configured to perform a predictor based correspondence search for each paxel in a current image of the pair of consecutive 2D images, wherein, for an anchor pixel in each paxel, the predictor based correspondence search evaluates a plurality of predictors to select a best matching pixel in a reference image of the pair of consecutive 2D images, and determine optical flow vectors for each pixel in a paxel based on the best matching pixel selected for the anchor pixel of the paxel.

The present disclosure describes a method, an apparatus, and a storage medium for recognizing an obstacle. The method includes acquiring, by a device, point cloud data obtained by scanning surroundings of a target vehicle by a sensor in the target vehicle. The device includes a memory storing instructions and a processor in communication with the memory. The method further includes converting, by the device, the point cloud data into a first image used for showing the surroundings; and recognizing, by the device, from the first image, a first object in the surroundings as an obstacle through a first neural network model.

Pixel image data of a scene is received in which the pixel image data includes a two-dimensional representation of an object in the scene. Point cloud data including three-dimensional point coordinates of a physical object within the scene corresponding to the two-dimensional representation of the object is received. The three-dimensional point coordinates include depth information of the physical object. The point cloud data is mapped to an image plane of the pixel image data to form integrated pixel image data wherein one or more pixels of the pixel image data have depth information integrated therewith. A three-dimensional bounding box is predicted for the object using a convolutional neural network based upon the integrated pixel image data.

This document describes techniques, apparatuses, and systems for sensor fusion for object-avoidance detection, including stationary-object height estimation. A sensor fusion system may include a two-stage pipeline. In the first stage, time-series radar data passes through a detection model to produce radar range detections. In the second stage, based on the radar range detections and camera detections, an estimation model detects an over-drivable condition associated with stationary objects in a travel path of a vehicle. By projecting radar range detections onto pixels of an image, a histogram tracker can be used to discern pixel-based dimensions of stationary objects and track them across frames. With depth information, a highly accurate pixel-based width and height estimation can be made, which after applying over-drivability thresholds to these estimations, a vehicle can quickly and safely make over-drivability decisions about objects in a road.