A method for creating a map for a self-moving device includes: acquiring a first image captured by the self-moving device in a working area in which the self-moving device moves, the first image being an image above the working area; extracting a target feature according to the first image, the target feature being configured to indicate a first plane directly above the self-moving device; determining an area contour of the working area based on the target feature; and generating an area map of the working area based on the area contour and first position information of the self-moving device in the working area. A self-moving device and an apparatus are also disclosed.

Systems and methods are provided for vehicle navigation. In one implementation, a navigation system for a host vehicle may include at least one processor programmed to receive from a camera onboard the host vehicle at least one captured image representative of an environment of the vehicle; detect a pedestrian represented in the at least one captured image; analyze the at least one captured image to determine an indicator of angular rotation and an indicator of pitch angle associated with a head of the pedestrian represented in the at least one captured image; and cause at least one navigational action by the host vehicle based on the indicator of angular rotation and the indicator of pitch angle associated with the head of a pedestrian.

A method for determining a distance between a cleaning robot and an obstacle. The cleaning robot has a first distance measuring system for a far measuring range and a second distance measuring system for a near measuring range. The second distance measuring system includes a camera. a) if the obstacle is located in the far measuring range, the distance between the cleaning robot and the obstacle is determined by way of the first distance measuring system. b) if the obstacle is located in an overlap region of the far measuring range and the near measuring range, the distance between the cleaning robot and the obstacle is determined by way of the first and the second distance measuring systems; c) if the obstacle is located in the near measuring range, the distance between the cleaning robot and the obstacle is determined by way of the second distance measuring system.

A computer-implemented method for calculating information relative to a relative speed between an objectand a camera, based on two images of the object acquired by the camera. The method comprises:determining a value of an optical flowbetween the two images and, altogether with or after the determination of the value the optical flow, determining at least one parameter of the transformation, using the optical flow; andcalculating information relative to a relative speed between the object and the camera, based on said at least one parameter of the transformation.

Techniques are discussed for determining a velocity of an object in an environment from a sequence of images (e.g., two or more). A first image of the sequence is transformed to align the object with an image center. Additional images in the sequence are transformed by the same amount to form a sequence of transformed images. Such sequence is input into a machine learned model trained to output a scaled velocity of the object (a relative object velocity (ROV)) according to the transformed coordinate system. The ROV is then converted to the camera coordinate system by applying an inverse of the transformation. Using a depth associated with the object and the ROV of the object in the camera coordinate frame, an actual velocity of the object in the environment is determined relative to the camera.

A method of providing surrounding information of a vehicle includes: detecting at least one sensor-based obstacle using sensor detection information acquired through a surroundings sensor, detecting at least one image-based obstacle using image information acquired through a plurality of cameras, detecting at least one matched obstacle from the at least one sensor-based obstacle or the at least one image-based obstacle, and correcting the position of the at least one matched obstacle using at least one of a sensor-based detection result or an image-based detection result with respect to the at least one matched obstacle.

The present disclosure discloses an engineering machinery equipment, and a method, system, and storage medium for safety control thereof, and relates to the field of artificial intelligence, automatic control, and engineering machinery technologies. A method can include: acquiring spatial sensing data of a work area; performing obstacle detection based on the spatial sensing data to determine a position of an obstacle within the work area; determining a safe working range of a mechanical structural component of the engineering machinery equipment based on the position of the obstacle within the work area; and controlling a working range of the mechanical structural component of the engineering machinery equipment based on the safe working range.

A method for detecting an obstacle, applied to a self-moving robot, including: transforming obstacle information into depth information; converting the depth information into a point cloud map, and determining coordinate data of a reference point on the obstacle; determining a valid analysis range in a height direction in the point cloud map; and determining, based on the coordinate data of the reference point, whether an obstacle is present within the valid analysis range.

Systems, devices, computer-implemented methods, and/or computer program products that can facilitate pedestrian detection via a boundary cylinder model are addressed. In one example, a system can comprise a processor that executes computer executable components stored in memory. The computer-executable components can comprise a bounding cylinder model component that determines numerical values for height and radius parameters of a three-dimensional bounding cylinder model representing a pedestrian, and that generates the three-dimensional bounding cylinder model representing the pedestrian based on the numerical values.

A map scanning method including the following step: scanning, by a scanning device, a location area of a vehicle to obtain location data, wherein the scanning device includes a lidar sensor, and the lidar sensor is configured to scan the location area to obtain a scan point cloud data of the location data; comparing, by a processor, the location data with a basic map to obtain a first point cloud area corresponding to the location data in the basic map; Comparing the scan point cloud data with the first point cloud area to calculate a matching degree value; and when the matching degree value is less than an update threshold, updating the first point cloud area according to the scan point cloud data.