Authors: Qi Wang; Chang-song Yang; Shaoen Wu
Addresses: School of Computer and Software; Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science and Technology, Nanjing 210044, China ' School of Automation; Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science and Technology, Nanjing 210044, China ' Department of Computer Science, Ball State University, Muncie, USA
Abstract: The application of global navigation satellite system (GNSS) is extensive in lane applications with the development of science and technology. Vision aided strapdown integrated navigation is an effective aided-navigation method in the case of GNSS failure in lane vehicles, which plays an important role in realising high-precision navigation of lane navigation system. A vision-aided navigation system based on GNSS positioning is constructed using the electrical powered platform as the research object. The hardware platform of vision navigation system is presented and digital image processing is used to segment the collected lane image. The image pre-processing operation, including denoising filtering and greyscale processing, is carried out to complete the segmentation and get the effective navigation area. According to the effective area of navigation, a navigation datum line is extracted by the least squares linear fitting and Hough transform. According to the camera imaging model and the camera's internal and external parameters, the navigation datum line in the image coordinates is transformed into the world coordinates, and the heading angle is calculated. Kalman filter algorithm is used to fuse the navigation parameters of the vision navigation module and the GNSS positioning module, and the integrated navigation model is established.
Keywords: lane vehicle; vision aided strapdown integrated navigation; image segmentation; navigation line detection; Kalman filter.
International Journal of Embedded Systems, 2020 Vol.13 No.1, pp.121 - 127
Received: 18 May 2019
Accepted: 20 Nov 2019
Published online: 11 May 2020 *