A method includes providing a fixture including a target in a field of view of a sensor mounted to a vehicle. The target is detectable by the sensor. The fixture includes a first rangefinding device and a second rangefinding device spaced from the first rangefinding device. The method includes measuring a first angle and first distance from the first rangefinding device to a first known point on the vehicle; measuring a second angle and second distance from the second rangefinding device to a second known point on the vehicle; determining a position and orientation of the target in a coordinate system relative to the vehicle based on the first angle, the first distance, the second angle, and the second distance; and calibrating the sensor based on the position and orientation of the target.

Systems, apparatuses, and methods for calibrating LiDAR sensors of a robot using intersecting LiDAR sensors are disclosed herein. According to at least one non-limiting exemplary embodiment, a robot may calibrate a calibration LiDAR based on a determined pose of the calibration LiDAR, wherein the pose is determined based on a measurement error between the calibration LiDAR and an intersecting reference LiDAR.

A method and apparatus for reducing magnetic tracking error in the position and orientation determined in an electromagnetic tracking system is disclosed. In some embodiments, a corrected position and orientation is blended with an uncorrected position and orientation based upon the calculated probability of each. To determine a corrected position and orientation, data from an IMU in the receiver is used to obtain a constraint on the orientation. In other embodiments, the amount of detected error due to electromagnetic distortion is measured. Any error is first assumed to be from “floor distortion,” and a correction is applied. If the error is still deemed too great, a constraint is again obtained from IMU data. Using this constraint, another correction for the distortion is made. The solution from this correction may be blended with a standard solution and the solution from the floor distortion to arrive at a final solution.

A method and apparatus for reducing magnetic tracking error in the position and orientation determined in an electromagnetic tracking system is disclosed. In some embodiments, a corrected position and orientation is blended with an uncorrected position and orientation based upon the calculated probability of each. To determine a corrected position and orientation, data from an IMU in the receiver is used to obtain a constraint on the orientation. In other embodiments, the amount of detected error due to electromagnetic distortion is measured. Any error is first assumed to be from “floor distortion,” and a correction is applied. If the error is still deemed too great, a constraint is again obtained from IMU data. Using this constraint, another correction for the distortion is made. The solution from this correction may be blended with a standard solution and the solution from the floor distortion to arrive at a final solution.

Embodiments of the present disclosure disclose a method for calibrating a relative pose, a device, and a medium. The method includes: obtaining first point cloud data of a scene collected by the laser radar in an automatic driving mobile carrier and first pose data collected by the navigation positioning system in the automatic driving mobile carrier; and determining the relative pose between the laser radar and the navigation positioning system based on the first point cloud data, the first pose data, second point cloud data pre-collected by a laser scanner in the scene and second pose data pre-collected by a positioning device in the scene.

In one embodiment, a system receives a stream of frames of point clouds from one or more LIDAR sensors of an ADV and corresponding poses in real-time. The system extracts segment information for each frame of the stream based on geometric or spatial attributes of points in the frame, where the segment information includes one or more segments of at least a first frame corresponding to a first pose. The system registers the stream of frames based on the segment information. The system generates a first point cloud map for the stream of frames based on the frame registration.

A position and posture estimation apparatus includes: a laser sensor configured to be disposed on at least one of left and right sides of a forklift, to emit laser light to a side surface of a pallet lifted by a fork, and to receive reflected light of the laser light and acquire a laser measurement point group; and an estimation calculation unit configured to estimate a position and a posture of the pallet with respect to the fork based on the laser measurement point group acquired by the laser sensor.

A computer is programmed to determine a localization of a first vehicle, including location coordinates and an orientation of the first vehicle, based on first vehicle sensor data, and to wirelessly receive localizations of respective second vehicles, wherein a first vehicle field of view at least partially overlaps respective fields of view of each of the second vehicles. The computer is programmed to determine pair-wise localizations for respective pairs of the first vehicle and one of the second vehicles, wherein each of the pair-wise localizations defines a localization of the first vehicle relative to a global coordinate system based on a (a) relative localization of the first vehicle with reference to the respective second vehicle and (b) a second vehicle localization relative to the global coordinate system, and to determine an adjusted localization for the first vehicle that has a minimized sum of distances to the pair-wise localizations.

A control method for a mobile object automatically moving includes: causing the mobile object to move along a first path; causing a sensor of the mobile object to detect a position and an attitude of a target object while the mobile object is moving along the first path; setting a second path up to a target position at which predetermined position and attitude with respect to the target object are achieved based on the position and the attitude of the target object; switching the first path to the second path to move the mobile object along the second path; executing optimization calculation based on an evaluation function, to set a third path; and switching the second path to the third path to move the mobile object along the third path.

A control method for a mobile object automatically moving includes: causing the mobile object to move along a first path; causing a sensor of the mobile object to detect a position and an attitude of a target object while the mobile object is moving along the first path; setting a second path up to a target position at which predetermined position and attitude with respect to the target object are achieved based on the position and the attitude of the target object; switching the first path to the second path to move the mobile object along the second path; executing optimization calculation based on an evaluation function, to set a third path; and switching the second path to the third path to move the mobile object along the third path.