Inertia/vision integrated navigation method adopting PSO (particle swarm optimization)-based CKF (cubature kalman filter)

A particle swarm optimization and integrated navigation technology, which is applied in the field of inertial/visual integrated navigation, can solve the problems that the visual navigation system cannot provide long-term high-precision navigation, and the accuracy of the inertial visual integrated navigation system decreases.

Inactive Publication Date: 2017-05-31
SOUTHEAST UNIV
View PDF6 Cites 17 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] In order to solve the shortcoming that the visual navigation system cannot provide high-precision navigation for a long time when the mobile robot is moving in a low-light or no-light environment, which leads to a

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Inertia/vision integrated navigation method adopting PSO (particle swarm optimization)-based CKF (cubature kalman filter)
  • Inertia/vision integrated navigation method adopting PSO (particle swarm optimization)-based CKF (cubature kalman filter)
  • Inertia/vision integrated navigation method adopting PSO (particle swarm optimization)-based CKF (cubature kalman filter)

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0064] Below in conjunction with accompanying drawing, the present invention is described in further detail:

[0065] The present invention is an inertial / visual combined navigation method using CKF (Cubature Kalman filters, volumetric Kalman filter) based on particle swarm optimization. The process is as follows figure 1 shown, including the following steps:

[0066] Step 1: When the visual signal is valid, use the camera on the mobile robot to collect dynamic video, and determine the speed of the camera through image feature extraction and nearest neighbor matching; use the SURF (Speeded Up Robust Features) algorithm to extract the video respectively SURF feature points in two adjacent image frames, and record the position coordinates of the feature points in the image coordinate system, and match the SURF feature points on the two frames of images according to the nearest neighbor matching method to determine whether the camera is on the horizontal plane The speed V x , V...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses an inertia/vision integrated navigation method adopting a PSO (particle swarm optimization)-based CKF (cubature kalman filter). The method comprises the following main steps: Step 1, when a visual signal is effective, acquiring a dynamic video with a camera carried by a mobile robot and determining the speed of the camera by an image feature extraction and nearest neighbor matching method; Step 2, calculating the speed of the mobile robot according to a course angle measured by an inertia navigation system and in combination with the speed of the camera and estimating the speed and the accelerated speed of the mobile robot with the CKF; Step 3, updating time and measurement with the CKF according to measurement values and status values at all time of the system and selected filter parameter values, so as to obtain an estimation value of a status of the system; Step 4, optimizing filter parameters Q and R by using a particle swarm algorithm according to current objective function values and filter parameter values at all time and taking an obtained modification value as an input parameter of the CKF until an optimal status estimation value is obtained.

Description

technical field [0001] The invention relates to the field of inertial / visual combined navigation, in particular to an inertial / visual combined navigation method using CKF based on particle swarm optimization. Background technique [0002] In recent years, with the rapid development of computer technology, electronic technology, communication technology, advanced control and artificial intelligence, the research and application of mobile robot technology have made great progress. As a complex comprehensive system integrating environmental perception, dynamic decision-making, and real-time behavior control and execution, intelligent mobile robots have been widely used in military, civilian, scientific research, and industrial production. Instead of humans to perform some work that needs to be carried out under harsh or dangerous conditions. Positioning and navigation, as the primary prerequisite for indoor mobile robots to complete tasks, have gradually become a research hots...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G01C21/16
CPCG01C21/165
Inventor 徐晓苏闫琳宇吴晓飞杨博
Owner SOUTHEAST UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap