Optimal partial rotation error for vehicle motion estimation based on omnidirectional camera

Abstract

This paper presents a method for robust motion estimation using an optimal partial rotation error based on spirits of the rotation averaging and the minimum spanning tree approaches. The advantage of an omnidirectional camera is that allows tracking landmarks over long-distance travel and large rotation of vehicle motions. The method does not process the optimal rotation at every frame due to the computational time, instead that, the optimal rotation error is applied for each interval of motion called partial motion so that the set of landmarks are tracked in all sequent images. This approach takes advantage of partial optimal error for reducing the divergences of estimated trajectory results in long-distance travel. The global motion of the vehicle is estimated in high accuracy based on utility of the optimal partial rotation error based on the rotation averaging method, which contrasts with traditional bundle adjustment using the minimum Euclid distance of back-projection errors. The experimental results demonstrate the effectiveness of this method under the large view scene in the outdoor environments.