Motion Estimation Based on Two Corresponding Points and Angular Deviation Optimization

Recently, there have been several studies on vision-based motion estimation under a supposition that planar motion follows a nonholonomic constraint. This allows reducing computational time. However, the vehicle motion in an outdoor environment does not accept this assumption. This paper presents a...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on industrial electronics (1982) 2017-11, Vol.64 (11), p.8598-8606
Main Authors: Hoang, Van-Dung, Le, My-Ha, Jo, Kang-Hyun
Format: Article
Language:English
Subjects:
Automotive parts
Bundle adjustment
Cameras
Computing time
Constraint modelling
Geometric constraints
Geometry
Motion estimation
Motion simulation
nonholonomic constraint
Optimization
Parameters
quasiconvex optimization
Roads
Steering
Three dimensional motion
Three-dimensional displays
Transmission line matrix methods
Vision
visual odometry (VO)
Visualization
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Recently, there have been several studies on vision-based motion estimation under a supposition that planar motion follows a nonholonomic constraint. This allows reducing computational time. However, the vehicle motion in an outdoor environment does not accept this assumption. This paper presents a method for estimating the vision-based 3-D motion of a vehicle with several parts as follows. First, the Ackermann steering model is applied to reduce constraint parameters of the 3-D motion. In difference to the previous contribution, the proposed approach requires only two corresponding points of consecutive images to estimate the vehicle motion. Second, motion parameters are extracted based on a closed-form solution on geometric constraints. Third, the estimation approach applies the bundle adjustment-based quasiconvex optimization. This task aims to take into account advantage of omnidirectional vision-based features for reducing errors. The omnidirectional vision supports for landmarks tracking in long travel and large rotation, which is appropriate for a bundle adjustment technique. Evaluated results show that the proposed method is applicable in the practical condition of outdoor environments.
ISSN:0278-0046
1557-9948
DOI:10.1109/TIE.2017.2703891