Feature coordinate estimation by time differenced vision/GPS/INS

Yu Dam Lee,Hyung Keun Lee,Won Jae Yoo,La Woo Kim,Kwang Ho Choi,D.D Wijaya

doi:10.1051/e3sconf/20199401005

Yu Dam Lee, Hyung Keun Lee + Show 4 more

Open Access

https://doi.org/10.1051/e3sconf/20199401005

Copy DOI

Journal: E3S Web of Conferences	Publication Date: Jan 1, 2019
License type: CC BY 4.0

Affiliation: Korea Aerospace University

Abstract

This paper proposes a multi-baseline method to estimate absolute coordinates of point clouds and the camera attitude parameter utilizing feature points in successive images. Conventionally, 3D map generation methodologies have been based on images acquired by aerial or land vehicles. Pixels corresponding to known landmarks are manually identified at first. Next, the coordinates are directly geo-referenced and automatically allocated to pixels with high-quality Global Positioning System (GPS) and Inertial Navigation System (INS). However, it is difficult to obtain accurate coordinates by the conventional methodology with low-cost GPS and INS. With camera positions and attitude parameters, image-based point clouds can be compensated accurately. A simulation was carried out to evaluate the performance of the proposed method.

Highlights

This paper proposes a method to estimate threedimensional (3-D) coordinates of point clouds and attitude parameters simultaneously by two-dimensional (2-D) matched feature points in two successive images
The proposed method to estimate absolute coordinates of point clouds and to compensate error of attitude parameters is briefly explained by pictures
Error of attitude parameters from Global Positioning System (GPS)/Inertial Navigation System (INS) Kalman outputs can be compensated by 2-D feature points extracted from the images

Summary

Introduction

This paper proposes a method to estimate threedimensional (3-D) coordinates of point clouds and attitude parameters simultaneously by two-dimensional (2-D) matched feature points in two successive images. To extract feature points and to match them in the two successive images, Speeded-Up Robust Feature (SURF) algorithm is utilized [4]. A loosely-coupled Kalman filter is applied to estimate absolute coordinates of the camera and its attitude parameters. With these parameters and error of them, image-based point clouds are compensated and converted to absolute coordinates of them. The proposed method to estimate absolute coordinates of point clouds and to compensate error of attitude parameters is briefly explained by pictures.

Absolute coordinates of point clouds with attitude error compensation

Findings

Simulation