DocumentCode :
3572606
Title :
Vision-aided inertial navigation using three-view geometry
Author :
Sen Wang ; Ling Chen ; Dongbing Gu ; Huosheng Hu
Author_Institution :
Sch. of Comput. Sci. & Electron. Eng., Univ. of Essex, Colchester, UK
fYear :
2014
Firstpage :
946
Lastpage :
951
Abstract :
This paper presents a novel unscented Kalman filter based algorithm for vision-aided inertial navigation system (VINS). It uses dynamic model of inertial measurement unit (IMU) to perform state propagation and trifocal tensor based geometric constraints of three views to update system. Unlike the conventional methods, the positions of feature points are neither required to be augmented into system state, nor estimated during initialization. The main contribution of this paper is twofold. First, a dynamic model which considers three-view geometry is derived for three-view based VINS. Second, it is the first time that trifocal tensor based geometric constraints and point transfer of three-view geometry are used for VINS, gaining robustness and avoiding scale ambiguity. The approach is experimentally evaluated by using a real IMU and image dataset that was recorded by a ground vehicle, verifying its effectiveness.
Keywords :
Kalman filters; inertial navigation; motion estimation; position measurement; tensors; geometric constraints; ground vehicle; image dataset; inertial measurement unit; state propagation; three-view geometry; trifocal tensor; unscented Kalman filter; vision-aided inertial navigation; Cameras; Feature extraction; Geometry; Quaternions; Robots; Tensile stress; Vectors; Vision-aided inertial navigation; inertial measurement unit; trifocal tensor; unscented Kalman filter;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Control and Automation (WCICA), 2014 11th World Congress on
Type :
conf
DOI :
10.1109/WCICA.2014.7052843
Filename :
7052843
Link To Document :
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