A method and system for automatically processing point cloud based on reinforcement learning are provided. The method for automatically processing point cloud based on reinforcement learning according to an embodiment of the present disclosure includes scanning to collect a point cloud (PCL) and an image through a lidar and a camera; calibrating, by a controller, to match locations of the image and the point cloud through reinforcement learning that maximizes a reward including geometric and luminous intensity consistency of the image and the point cloud; and meshing, by the controller, the point cloud into a 3D image through reinforcement learning that minimizes a reward including a difference between a shape of the image and a shape of the point cloud.

A target alignment system for calibrating a safety sensor mounted on a vehicle with front and rear wheels by locating an optimum target position upon a horizontal surface for accurate calibration of the sensor. The target alignment system comprises a plurality of visual guide projectors and a pair of target assemblies which project a visible guide line perimeter around the vehicle, the perimeter including parallel longitudinal lines on either side of the vehicle, a lateral alignment guide line crossing the longitudinal lines in front of the vehicle, and a center guide line colinear with the vehicle center line. The front and rear wheels of the vehicle are longitudinally aligned causing the vehicle thrust line to match the vehicle center line. One of the visual guide projectors projects a transverse line across the center guide line, creating an intersection point which marks the optimum target position.

A system and method for guiding placement of a vehicle service external fixture relative to a vehicle undergoing service or inspection. A vehicle service system support structure having at least one camera module is positioned at an initial location within a vehicle service area, and a location of the initial location within a vehicle reference frame is established from images of optical targets secured to the vehicle. The vehicle service system support structure is subsequently repositioned relative to the vehicle to a new position located outside of an external fixture placement region, while maintaining at least one of the observed optical targets within a field of view of the camera module. The new position of the vehicle service system support structure within said vehicle reference frame is determined from target images, and a placement location within the placement region for the external fixture is identified relative to the vehicle.

An electronic device may include an infrared light source and an infrared image sensor to enable infrared beacon functionality. In a location sharing scenario, a first electronic device may use the infrared light source to emit infrared light and serve as an infrared beacon. A second electronic device may use the infrared image sensor to detect the infrared beacon and identify the location of the first electronic device. The infrared image sensor that is used to detect the infrared beacon may also serve as a time-of-flight sensor for a light detection and ranging (LiDAR) module. The second electronic device (that detects the infrared beacon) may provide output such as visual, audio, and/or haptic output to inform a user of the location of the infrared beacon.

Embodiments of the present disclosure disclose a data acquisition device, a data correction method and apparatus, and an electronic device. The data acquisition device includes: a rotation component, a first ranging component, and an image acquisition component. The rotation component is configured to drive the data acquisition device to rotate in a first direction. The first ranging component is configured to rotate in the first direction along with the data acquisition device, to rotate in a second direction, and to measure first ranging data. The first direction is different from the second direction. The image acquisition component is configured to rotate in the first direction along with the data acquisition device, and to acquire image data in a three-dimensional scene.

A method for calibrating a Lidar and a positioning device, a device, and a storage medium. The method includes: acquiring a point-cloud data sequence of the Lidar and a pose data sequence of the positioning device, in which, the Lidar and the positioning device are on a same traveling device; determining first trajectory information of the Lidar and second trajectory information of the positioning device according to the point-cloud data sequence and the pose data sequence; and determining a calibration offset between the Lidar and the positioning device according to the first trajectory information and the second trajectory information, in which, a matching degree between the first trajectory information and the second trajectory information satisfies a preset matching degree condition under a trajectory information correspondence determined based on the calibration offset.

A sensor device having a sensor housing and a printed circuit board coupled to the sensor housing. A light emitting device is coupled to the printed circuit board. The light emitting device has an emitter face defining an emission face area. An aperture plate is coupled to the sensor housing, the aperture plate defines an aperture having an aperture area that is less than the emission face area of the emitter face. The aperture is less than 1 mm from the emitter face wherein the light emitting device is not fixed to the aperture plate. A lens is coupled to the sensor housing, having an optical axis extending through the aperture. The aperture plate is positioned between the lens and the emitter face. Boresighting angle variation across sensor components on a manufacturing line may advantageously be reduced without increased cost associated with active alignment. Irradiance drop-out may also be reduced.

A method for calibrating and/or adjusting a lidar system. In the method, in order to perform a measurement-based comparison with respect to an underlying one-dimensionally or two-dimensionally detecting detector unit, a distribution of secondary light incident from the field of view and imaged onto the detector unit, and a center position and/or width of the distribution is/are acquired as position data and compared especially with presumed and/or expected position data featuring an expected center position and/or an expected distribution.

Described herein are systems, methods, and non-transitory computer readable media for performing an alignment between a first vehicle sensor and a second vehicle sensor. Two-dimensional (2D) data indicative of a scene within an environment being traversed by a vehicle is captured by the first vehicle sensor such as a camera or a collection of multiple cameras within a sensor assembly. A three-dimensional (3D) representation of the scene is constructed using the 2D data. 3D point cloud data also indicative of the scene is captured by the second vehicle sensor, which may be a LiDAR. A 3D point cloud representation of the scene is constructed based on the 3D point cloud data. A rigid transformation is determined between the 3D representation of the scene and the 3D point cloud representation of the scene and the alignment between the sensors is performed based at least in part on the determined rigid transformation.

The present invention relates to a director mount arrangement for automatic alignment of a subsystem relative to a platform, wherein said director mount arrangement is arranged to pivotably support the subsystem. The director mount arrangement comprises a pivot frame arrangement and a control system. The control system comprises a control unit arranged to generate control signals so as to control the orientation of and stabilize the subsystem. The control signals are generated based on angular rate of subsystem and orientation operating commands provided from an operator. The control unit further generates estimated control signals based on platform orientation information and determine a difference between the control signals and the estimated control signals, wherein the difference is indicative of mechanical misalignments between the subsystem and the platform. The control unit further generates alignment corrections based on the determined difference so as to automatically align the subsystem relative to the platform.