The purpose is to provide an image generating device which generates a synthesized image from which one is able to intuitively grasp a relation between an image and a traveling position of a water-surface movable body. The image generating device includes processing circuitry. The processing circuitry acquires attitude information indicative of an attitude of a camera or a ship where the camera is installed. The processing circuitry acquires a traveling route of the ship based on a detection result of at least one of a position and a direction of the ship. The processing circuitry generates traveling route display data based on the attitude information and the traveling route. The processing circuitry generates a synthesized image in which the traveling route display data is synthesized with an image outputted from the camera.

Systems and methods for tracking a heading of an excavator are provided. An initial heading of the excavator platform is obtained and a current azimuthal orientation of the excavator platform is associated with the initial heading. Coordinates of a measurement center of the GNSS device are obtained. Coordinates of a center of rotation of the excavator platform are determined using the initial heading of the excavator platform, the coordinates of the measurement center, and a known spatial relationship between the measurement center of the GNSS device and the center of rotation of the excavator platform. Rotation of the excavator platform is tracked from the initial heading to a first heading using rotation measurements from a swing sensor.

A method of automatic labeling of images for supervised machine learning includes obtaining images of roadside objects with a camera mounted to a vehicle, recording a position and orientation of the vehicle within a defined coordinate system while obtaining the images recording position information for each roadside object with the same defined coordinates system as used while recording the position and orientation of the vehicle, and correlating a position of each of the obtained images of the roadside objects with the position information of each roadside object in view of the recorded position and orientation of the vehicle. The images are labeled to identify the roadside objects in view of the correlated position of each of the obtained images of the roadside objects.

A LIDAR-to-vehicle alignment system includes a memory and an autonomous driving module. The memory stores points of data provided based on an output of a LIDAR sensor and GPS locations. The autonomous driving module performs an alignment process including performing feature extraction on the points of data to detect one or more features of one or more predetermined types of objects having one or more predetermined characteristics. The features are determined to correspond to one or more targets because the features have the predetermined characteristics. One or more of the GPS locations are of the targets. The alignment process further includes: determining ground-truth positions of the features; correcting the GPS locations based on the ground-truth positions; calculating a LIDAR-to-vehicle transform based on the corrected GPS locations; and based on results of the alignment process, determining whether one or more alignment conditions are satisfied.

A LIDAR-to-vehicle alignment system includes a memory and an autonomous driving module. The memory stores points of data provided based on an output of a LIDAR sensor and GPS locations. The autonomous driving module performs an alignment process including performing feature extraction on the points of data to detect one or more features of one or more predetermined types of objects having one or more predetermined characteristics. The features are determined to correspond to one or more targets because the features have the predetermined characteristics. One or more of the GPS locations are of the targets. The alignment process further includes: determining ground-truth positions of the features; correcting the GPS locations based on the ground-truth positions; calculating a LIDAR-to-vehicle transform based on the corrected GPS locations; and based on results of the alignment process, determining whether one or more alignment conditions are satisfied.

A system and method for determining attitude of an end point equipment (EPE) using a global navigation satellite system (GNSS) receiver. The method includes collecting signals and radio frequency (RF) switch states, wherein the signals are GNSS signals received by at least one GNSS antenna of an end point equipment (EPE), wherein the signals are associated with the respective RF switch states; generating differencing data of the signals with respect to reference measurements, wherein the reference measurements are collected from a GNSS receiver at a reference station in a predetermined distance from the EPE; determining an attitude of the EPE based on the generated differencing data; and causing reorientation of the EPE based on the determined attitude.

A system and method for determining attitude of an end point equipment (EPE) using a global navigation satellite system (GNSS) receiver. The method includes collecting signals and radio frequency (RF) switch states, wherein the signals are GNSS signals received by at least one GNSS antenna of an end point equipment (EPE), wherein the signals are associated with the respective RF switch states; generating differencing data of the signals with respect to reference measurements, wherein the reference measurements are collected from a GNSS receiver at a reference station in a predetermined distance from the EPE; determining an attitude of the EPE based on the generated differencing data; and causing reorientation of the EPE based on the determined attitude.

The present disclosure relates to vehicle positioning methods, apparatus, controllers, intelligent vehicles, and systems. One example vehicle positioning method includes obtaining a first relative pose between a first vehicle and a help providing object, obtaining a global pose of the help providing object, and calculating a global pose of the first vehicle based on the first relative pose and the global pose.

Systems and methods for use in navigating aircraft are provided. The systems can use Geometry Redundant Almost Fixed Solutions (GRAFS) or Geometry Extra Redundant Almost Fixed Solutions (GERAFS) to compute high confidence error bounds for a heading angle estimate or pitch angle derived using signals received on at least two antennas.

Systems and methods for use in navigating aircraft are provided. The systems can use Geometry Redundant Almost Fixed Solutions (GRAFS) or Geometry Extra Redundant Almost Fixed Solutions (GERAFS) to compute high confidence error bounds for a heading angle estimate or pitch angle derived using signals received on at least two antennas.