Object localization method, application and vehicle

Abstract

The invention provides an object positioning method, application and vehicle. The object positioning method comprises the following steps: taking a fixed point outside the object as the origin to establish the rectangular coordinate system XoY of the object, wherein the plane where the X-axis and the Y-axis of the rectangular coordinate system are located is a horizontal plane , take any point on the object as a reference point, and take any direction as a reference direction, and compare the x-coordinate, y-coordinate, and the reference direction of the reference point on the rectangular coordinate system with those in the plane The heading angle θ enclosed by a fixed straight line, the distance s traveled by the object in unit time, and the turning curvature c of the object are taken as the state vector, wherein the reference point is at a moment k x-coordinate x k , y coordinate y k By the x-coordinate x of k‑1 at the previous moment k‑1 , y coordinate y k‑1 , heading angle θ k‑1 , distance s k‑1 , turning curvature c k‑1 come and get.

Description

technical field [0001] The invention relates to the field of positioning, in particular to a vehicle low-speed local positioning algorithm based on extended Kalman filter, in particular to an object positioning method, its application in the parking process and the corresponding vehicle. Background technique [0002] In the field of positioning, for example, CN106610294A discloses a positioning method and device. Wherein, the positioning method is used for indoor positioning of the vehicle, and the positioning method includes: obtaining the geographic location, driving speed and angular velocity of the vehicle at a certain moment; performing dead reckoning based on the geographic location, driving speed and angular velocity of the vehicle at this moment, and obtaining the current position of the vehicle. The estimated geographic location of the moment. A correction to the extrapolated geolocation may be implemented using a Kalman filter to improve the accuracy of the vehicl...

AI Technical Summary

Problems solved by technology

Furthermore, it does not deal with Extended Kalman Filtering, i.e. the Jacobian matrix required to transform a nonlinear system into a linear system

[0004] In the automatic parking system, the positioning algorithm based on relatively low-cost sensors (wheel speed sensor and steering wheel angle sensor) plays a vital role in the realization of the parking function, and the positioning method based on the wheel speed sensor is less affected by ground conditions. Large, positioning on uneven ground can be challenging

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