Technologies and techniques for vehicle perception. A contour extractor apparatus extracts contours from image data. Mapping offsets are determined between at least some of the grid points of a current image map and respective grid points of a next image map, and image velocity vectors are determined for at least some of mapping offsets. At least some of the grid points of the next image are warped to the respective grid points of the current image map. A determination is made if a contour of the warped grid points of the next image matches a contour of the respective grid points of the current map within a configured parameter. Object movement movement may then be determined from the image data, if the contour of the warped grid points of the next image matches the contour of the respective grid points of the current map within the configured parameter.

An image processing device includes: an imaging unit including an optical system that forms an optical image including a low-distortion region and a high-distortion region on a light receiving surface and configured to image a side behind a first moving device; an image processing unit configured to generate image data by performing distortion correction on imaged data generated by the imaging unit; a display control unit configured to cause first image data corresponding to the low-distortion region in the image data to be displayed; and a detection unit configured to detect a second moving device that satisfies a predetermined condition from the image data corresponding to a region including the high-distortion region, and the display control unit causes second image data including the second moving device corresponding to the high-distortion region to be displayed in a case where the detection unit detects the second moving device.

An aerial robot includes an image sensor for capturing images of an environment. The robot receives a first image captured at a first location. The robot identifies one or more first pixels in the first image. The first pixels correspond to one or more targeted features of an object identified in the first image. The robot receives a second image captured at the second location. The robot receives its distance data that estimates a movement of the robot from the first location to the second location. The robot identifies second pixels in the second image. The second pixels corresponding to the targeted features of the object as appeared in the second image. The robot determines an estimated distance between the robot and the object based on the changes of locations of the second pixels from the first pixels relative to the movement of the robot provided by the distance data.

An autonomous mobile device (AMD) may move around a physical space while performing tasks. Sensor data is used to determine an occupancy map of the physical space. Some objects within the physical space may be difficult to detect because of characteristics that result in lower confidence in sensor data, such as transparent or reflective objects. To include difficult-to-detect objects in the occupancy map, image data is processed to identify portions of the image that includes features associated with difficult-to-detect objects. Given the portion that possibly includes difficult-to-detect objects, the AMD attempts to determine where in the physical space that portion corresponds to. For example, the AMD may use stereovision to determine the physical area associated with the features depicted in the portion. Objects in that area are included in an occupancy map annotated as objects that should persist unless confirmed to not be within the physical space.

A system for a vehicle is provided. The system may include a memory and at least one processor configured to: access a plurality of images of a forward-facing view from the vehicle, the plurality of images corresponding to image data obtained by a camera; determine from the images a first lane marking on a first side of a lane, the lane through which the vehicle can navigate, and a second lane marking on a second side of the lane opposite of the first side; navigate the vehicle autonomously relatively centered between the first and second lane markings; determine from the plurality of images that an object is on the first side or the second side of the lane, and the object beyond the first or second lane marking; and navigate the vehicle autonomously to travel over a driving path that is offset from a center of the lane.

Controlling an unmanned aerial vehicle to traverse a portion of an operational environment of the unmanned aerial vehicle may include obtaining an object detection type, obtaining object detection input data, obtaining relative object orientation data based on the object detection type and the object detection input data, and performing an object avoidance operation based on the relative object orientation data. The object detection type may be monocular object detection, which may include obtaining the relative object orientation data by obtaining motion data indicating a change of spatial location for the unmanned aerial vehicle between obtaining the first image and obtaining the second image based on searching along epipolar lines to obtain optical flow data.

An information processing device includes: a processor configured to: collect monitoring information indicating circumstances of a monitoring area from a plurality of monitoring information acquiring units, detect an object which is located in the monitoring area on the basis of the monitoring information, update map information of the monitoring area using position information indicating a position of the object, and provide the map information to a working machine that performs predetermined work in the monitoring area.

A system and method for estimating free space including applying a machine learning model to camera images of a navigation area, where the navigation area is broken into cells, synchronizing point cloud data from the navigation area with the processed camera images, and associating probabilities that the cell is occupied and object classifications of objects that could occupy the cells with cells in the navigation area based on sensor data, sensor noise, and the machine learning model.

A sensor calibrator comprising one or more processors configured to receive sensor data representing a calibration pattern detected by a sensor during a period of relative motion between the sensor and the calibration pattern in which the sensor or the calibration pattern move along a linear path of travel; determine a calibration adjustment from the plurality of images; and send a calibration instruction for calibration of the sensor according to the determined calibration adjustment. Alternatively, a sensor calibration detection device, comprising one or more processors, configured to receive first sensor data detected during movement of a first sensor along a route of travel; determine a difference between the first sensor data and stored second sensor data; and if the difference is outside of a predetermined range, switch from a first operational mode to a second operational mode.

A vehicular trailer hitching assist system includes a rear backup camera disposed at a rear portion of a vehicle and a control disposed in the vehicle. A display device includes a video display screen operable to display video images for viewing by a driver of the vehicle. Responsive to the driver initiating a hitching maneuver event by engaging reverse gear of the vehicle, rear backup video images derived from image data captured by the rear backup camera are displayed at the video display screen. An overlay overlays the displayed rear backup video images and aids in guiding connection of a trailer hitch of the equipped vehicle to a trailer tongue of a trailer. Continued display of rear backup video images that are overlaid by the overlay is ceased upon elapse of a threshold period of time or upon exceeding a threshold speed or upon exceeding a threshold distance traveled.