A localization and attitude estimation method using magnetic fields includes the following steps. First, in three-dimensional coordinates, at least three magnetic landmarks arbitrarily disposed around a moving carrier are selected, wherein any two of the at least three magnetic landmarks have different magnetic directions. One set of at least five tri-axes magnetic sensors is used to sense the magnetic fields of the at least three magnetic landmarks. Three magnetic components on three axes of a current position of each of the tri-axes magnetic sensors are respectively generated by a demagnetization method. Five non-linear magnetic equations are solved to obtain position information and magnetic moment information of the at least three magnetic landmarks in the three-dimensional coordinates. Position vectors and attitude vectors of the set of at least five tri-axes magnetic sensors in a three-dimensional space are estimated based on tri-axes magnetic moment vectors of the magnetic landmarks.

A navigation planning method and apparatus comprising a chart data receiving terminal configured to receive a chart data including position information of one or a plurality of targets; a waypoint receiving terminal configured to receive a plurality of waypoints including a latest waypoint for a navigation route of a movable body; a potential waypoint receiving terminal configured to receive a potential waypoint being movable on the chart; and processing circuitry configured to: determine a position of the potential waypoint as a next waypoint following the latest waypoint, the potential waypoint being movable, wherein the potential waypoint is determined by: receive a current position data of the potential waypoint; receive position information of the plurality of targets located within a predetermined distance from the current position of the potential waypoint; calculate an angle between a first bar and a second bar on the chart, wherein the first bar is to connect the latest waypoint with the potential waypoint and the second bar is to connect the potential waypoint with the plurality of targets; and output an activating signal when the calculated angle is equal to a predetermined value.

The disclosure relates to a method for determining correction values for a number of sensors of a traveling motor vehicle. The method being based on backward calculation. The disclosure further relates to a method for determining a position of a motor vehicle, using the correction values. The disclosure also relates to an associated electronic control device and to an associated non-volatile computer-readable storage medium.

The disclosure relates to a method for determining correction values for a number of sensors of a traveling motor vehicle. The method being based on backward calculation. The disclosure further relates to a method for determining a position of a motor vehicle, using the correction values. The disclosure also relates to an associated electronic control device and to an associated non-volatile computer-readable storage medium.

An aerial-and-ground data combined gravity conversion method includes the following steps: calculate the first estimated ground gravity by the Runge-Kutta format 1, and calculate the first error between the first estimated ground gravity and the measured ground gravity; calculate the second estimated ground gravity by the Runge-Kutta format 2, and calculate the second error between the second estimated ground gravity and the measured ground gravity; and select the smaller one from the first and second errors, use the corresponding Runge-Kutta format as the Runge-Kutta format for gravity conversion, and finish the gravity data conversion using the mentioned Runge-Kutta format.

An aerial-and-ground data combined gravity conversion method includes the following steps: calculate the first estimated ground gravity by the Runge-Kutta format 1, and calculate the first error between the first estimated ground gravity and the measured ground gravity; calculate the second estimated ground gravity by the Runge-Kutta format 2, and calculate the second error between the second estimated ground gravity and the measured ground gravity; and select the smaller one from the first and second errors, use the corresponding Runge-Kutta format as the Runge-Kutta format for gravity conversion, and finish the gravity data conversion using the mentioned Runge-Kutta format.

A camera produces image data representing an image of a surrounding view of a watercraft. A controller obtains the image data. The controller recognizes an image representing a specific mark in the image data. With reference to a control data set, the controller obtains watercraft operating information associated with the specific mark recognized in the image data. The control data set defines a relationship between the specific mark and the watercraft operating information.

A location and governance system and method for light electric vehicles that includes on-board sensors and receivers for providing readings used to compute absolute and relative vehicle position information, and combining the absolute and relative position information to compute a determined vehicle position, and a current surface type being traveled on by the vehicle. Governance commands for the vehicle can be generated based on the current surface type. Positioning system receivers, inertial measuring units, cameras and other sensor can be used. Vibration analysis, image processing, transition detection and other methods can be used to determine vehicle position and surface type, and spatial databases and other resources can be used. Determining a current surface type the vehicle is travelling on can include determining whether the vehicle is traveling on a sidewalk.

A vehicle movement tracking system that employs floor mats having ridges for generating location information in the form of modulated vibrations, detectable with an accelerometer. Two sensors are in a wheel of a vehicle. One sensor senses wheel rotation, and the other sensor senses vertical acceleration. The vehicle passes over a floor mat comprising vertically elevated ridges thereon that code the mat and thereby indicate the location at which the mat is at. When the vehicle travels over this mat the vertical acceleration sensor in the wheel detects the vertically elevated ridges and the wheel rotation sensor detects the distance between the vertically elevated ridges. In combination these two sensors are used to create a location word that denotes the mat over which the vehicle passes over. The location word is stored in non-volatile memory and later uploaded to a location collection station.

A method and system of magnetic fingerprint based neural network training for mobile device indoor navigation and positioning. The method, executed in a processor of a server computing device, comprises determining, in the processor, at a plurality of locations, a set of magnetic input parameters in accordance with a magnetic infrastructure profile of at least a portion of an indoor area, the processor implementing an input layer of a neural network, the set of magnetic input parameters providing a magnetic feature input to the input layer of the neural network; receiving, from a mobile device positioned at the first location, a set of measured magnetic parameters at respective ones of the plurality of locations; computing, at an output layer of the neural network implemented by the processor, an error matrix based on comparing an initial matrix of weights associated with the at least a first neural network layer representing the magnetic feature input to a magnetic feature output in accordance with the magnetic measured parameters of the mobile device; and recursively adjusting the initial weights matrix by backpropogation to diminish the error matrix until the generated magnetic feature output matches the magnetic measured parameters.