An image processing system includes: a possibility determination unit that determines whether there is a possibility that an obstacle is shown in an image acquired by an image acquisition device of a vehicle; a transmitting unit that transmits, from the vehicle, image information of the acquired image if it has been determined that there is a possibility that an obstacle is shown therein; a receiving unit that receives the image information from vehicles; a processing unit that performs image processing to identify an obstacle shown in the acquired image; and a duplication determination unit that determines whether or not an identified obstacle, which has been identified in a previous acquired image, is shown in a subsequent acquired image received by the receiving unit, wherein the processing unit performs the image processing on the subsequent acquired image if the subsequent acquired image has been determined to not show the identified obstacle.

An information processing apparatus capable of detecting a plane constituting a movement-enabling region. A normal direction of a plane constituting a road surface is detected on the basis of polarized images in multiple polarizing directions acquired by a polarization camera. A laser ranging sensor measures a distance to a point on the road surface so as to measure a position of the point. The plane constituting the road surface is identified on the basis of information regarding the normal direction of the plane constituting the road surface and information regarding the position of the point on the road surface.

Provided are a laser coarse registration method, device, mobile terminal and storage medium, the method comprises: determining the position information of obstacles around a robot; converting the position information of the obstacle into an obstacle image; acquiring map data of a pre-established probability map; converting the map data into a map image; carrying out matching processing on the obstacle image and the map image through a convolutional neural network to obtain a coarse registration position of the robot on the map image. The laser coarse registration method carries out convolution matching processing on the obstacle image and the map image obtained by the robot through the convolutional neural network, achieves the coarse registration of laser data collected by the robot and the probability map, the registration range is expanded, and the precision of coarse registration is improved.

An apparatus, system and method of operating an autonomous mobile robot having a height of at least one meter. The apparatus, system and method may include a robot body; at least two three-dimensional depth camera sensors affixed to the robot body proximate to the height, wherein the at least two three-dimensional depth camera sensors are both directed toward a major floor surface from the affixation and, in combination, comprise an at least substantially 360 degree field of view of the major floor surface around the robot body; and a processing system for receiving of data within the field of view from the at least one three-dimensional depth camera sensor, detecting the presence of a plurality of AR tags on the upper surface of the charging base, calculating a virtual alignment point associated with the center of the robot docking connector, calculating a virtual alignment point associated with the center of the charging base docking connector, and outputting a path of travel between the center of the charging base docking connector and the center of the robot docking connector, whereby a physical connection is made.

An obstacle recognition method and apparatus, a computer device, and a storage medium are provided. The method comprises: acquiring point cloud data scanned by LiDAR and time-sequence pose information of a vehicle; determining a spliced image of an eye bird view according to the point cloud data, the time-sequence pose information, and a historical frame embedded image; inputting the spliced image into a preset first CNN model to obtain a current frame embedded image and pixel-level information of the eye bird view; determining recognition information of at least one obstacle according to the current frame embedded image and pixel-level information.

A target detection method based on the fusion of prior positioning of a millimeter-wave radar and a visual feature includes: simultaneously obtaining, based on the millimeter-wave radar and a vehicle-mounted camera after being calibrated, point cloud data of the millimeter-wave radar and a camera image; performing spatial 3D coordinate transformation on the point cloud data to project transformed point cloud data onto a camera plane; generating a plurality of anchor samples based on projected point cloud data according to a preset anchor strategy, and obtaining a final anchor sample based on a velocity-distance weight of each candidate region; fusing RGB information of the camera image and intensity information of an RCS in the point cloud data to obtain a feature of the final sample; and inputting the feature of the final anchor sample into a detection network to generate category and position information of a target in a scenario.

A computer vision method performed by a computing system includes: receiving an image; identifying a light intensity value for each pixel of a set of pixels of the image; defining a light intensity band formed by an upper light intensity threshold and a lower light intensity threshold based on a light intensity distribution of the light intensity values of the set of pixels; for each pixel of the set of pixels, identifying whether that pixel has a light intensity value that is within the light intensity band or outside of the intensity band; and generating a modified image by increasing a light intensity contrast between a first subset of pixels identified as having light intensity values within the light intensity band and a second subset of pixels identified as having light intensity values outside of the light intensity band.

A vehicle includes an alarm portion, a storage configured to store information on a ground clearance, a camera configured to obtain a surrounding image including a front image and a rear image of the vehicle, and a controller configured to, when an object in front of the vehicle is recognized based on the front image obtained by the camera, determine a height of the object based on the front image obtained by the camera, and when the ground clearance is greater than the height of the object by comparing the height of the object with the ground clearance, and when the object is not recognized in a rear side of the vehicle based on the rear image obtained by the camera, configured to control the alarm portion to output a warning alarm.

An input apparatus for a vehicle according to the present disclosure includes an image output device that outputs an image block including a predetermined vehicle control command image, an image input device that photographs the image block to recognize a position of the image block, an object detection device that detects an object on the image block, and a controller that generates a matrix coordinate which is mapped to correspond a position detection depending on a sensing signal of the object detection device on a position of the image block recognized through the image input device, and then that executes a vehicle control command corresponding to a matrix coordinate of a position where the object is positioned when the object detection device detects the object on the image block.

Provided is an in-vehicle stereo camera which enables the continuation of automated driving if failures occur during imaging. A pair of captured images 301, 302, which is captured by a pair of imaging units so that both contain an overlapping region, are acquired (S201), it is determined whether an abnormal region is present in at least one of the pair of captured images, and if an abnormal region is present, the degree of impact from the abnormal region on an object recognition process of an object recognition unit is diagnosed (S202), and the processing content of the object recognition process is updated according to the degree of impact (S203).