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.

The present teaching relates to method, system, medium, and implementations for sensor calibration. A request is received from an ego vehicle in motion on a route for assistance in collaborative calibration of a sensor deployed on the ego vehicle. The request specifies a position of the ego vehicle and a configuration of the sensor with respect to the ego vehicle. A collaborative means along the route is identified based on the ego vehicle's position, the configuration of the sensor, the route, and the position associated with the collaborative means. A calibration assistance package is generated in response to the request and sent to the ego vehicle. The calibration assistance package includes information about the collaborative means that can be used to identify the collaborative means while in motion along the route for calibrating the sensor while the ego vehicle is in the vicinity of the collaborative means.

A vehicle detecting device includes: a camera that includes an image sensor and a first filter, the image sensor including a plurality of imaging elements including a first imaging element group and a second imaging element group, the first filter keeping an amount of light entering the first imaging element group lower than an amount of light entering the second imaging element group; an image generating unit that generates a first image based on information obtained from the first imaging element group and a second image based on information obtained from the second imaging element group; and a detecting unit that detects a front vehicle based on the first image and the second image.

An apparatus for providing an around view comprises: first and second cameras for acquiring first and second image information; and a processor. The processor can generate an around view image including a first area including a subject overlapping area and a second area excluding the subject overlapping area, on the basis of first and second images. The subject overlapping area is disposed between first and second boundary lines in the first and second images. The first boundary line is located in the first area, and the second boundary line adjoins the second area. If it is predicted that a first object will pass through the second boundary line due to the first object approaching within a predetermined distance thereto, the processor can generate an around view image in which the second area is changed so that the second area includes the subject overlapping area.

A LiDAR point cloud that includes two candidate clusters for merging is received. At a first phase, a distance between the two clusters is determined. If the distance is greater than a threshold, the candidate clusters are not merged. Otherwise, an additional point cloud is received for each cluster at different times. A motion characteristic is determined for each cluster. If the motion characteristic for each cluster is close (indicating that the objects are moving at the same speed), then the clusters are merged. Otherwise the clusters are not merged. The motion characteristic for a cluster can be determined by performing an alignment operation using the point cloud received for the cluster, and using the error associated with the alignment operation as the motion characteristic for the cluster. The decision to merge clusters is based on raw point cloud data, which can take place early in the tracking cycle.

Embodiments is disclosed to detect a locked heading direction of a pedestrian and to predict a path for the pedestrian using the locked heading direction. According to one embodiment, a system perceives an environment of an autonomous driving vehicle (ADV) using one or more image capturing devices. The system detects a pedestrian in the perceived environment. The system determines a facing direction of the pedestrian relative to the ADV as one of left/right side, front, or back. If the facing direction of the pedestrian is determined to be front or back facing, the system determines a lane nearest to the pedestrian. The system projects the pedestrian onto the nearest lane to determine a lane direction at the projection. The system determines a heading direction for the pedestrian locking to the lane direction of the nearest lane based on a predetermined condition.

A method for detecting objects and labeling the objects with distances in an image includes steps of: obtaining a thermal image from a thermal camera, an RGB image from an RGB camera, and radar information from an mmWave radar; adjusting the thermal image based on the RGB image to generate an adjusted thermal image, and generating a fused image based on the RGB image and the adjusted thermal image; generating a second fused image based on the fused image and the radar information; detecting objects in the images, and generating, based on the fused image, another fused image including bounding boxes marking the objects; and determining motion parameters of the objects.

The present disclosure provides an image-viewing method, a terminal, and a cleaner.

A system and method for performing visual odometry is disclosed. In aspects, the system implements methods to generate an image pyramid based on an input image received. A refined pose prior information representing a location and orientation of the autonomous vehicle can be generated based on one or more images of the image pyramid. One or more seed points can be selected from the one or more images of the image pyramid. One or more refined seed representing the one or more seed points with added depth values can be generated. One or more scene points can be generated based on the one or more refined seed points. A point cloud can be generated based on the one or more scene points.

A method and an electronic apparatus for predicting a path based on an object interaction relationship are provided. The method includes the following. A video including a plurality of image frames is received. Object recognition is performed on a certain image frame among the plurality of image frames to recognize at least one object in the certain image frame. Preset interactive relationship information associated with the at least one object is obtained from an interactive relationship database based on the at least one object. A first trajectory for navigating the first vehicle is determined based on the preset interactive relationship information.