Show simple item record

dc.contributor.authorCáceres Hernández, Danilo
dc.contributor.authorDung Hoang, Van
dc.contributor.authorHyun Jo, Kang
dc.date.accessioned2018-06-27T21:04:26Z
dc.date.available2018-06-27T21:04:26Z
dc.date.issued2013-07-28
dc.identifier.urihttp://ridda2.utp.ac.pa/handle/123456789/5082
dc.descriptionIn recent years, classical structure from motion based SLAM has achieved significant results. Omnidirectional camera-based motion estimation has become interested researchers due to the lager field of view. This paper proposes a method to estimate the 2D motion of a vehicle and mapping by using EKF based on edge matching and one point RANSAC. Edge matching based azimuth rotation estimation is used as pseudo prior information for EKF predicting state vector. In order to reduce requirement parameters for motion estimation and reconstruction, the vehicle moves under nonholonomic constraints car-like structured motion model assumption. The experiments were carried out using an electric vehicle with an omnidirectional camera mounted on the roof. In order to evaluate the motion estimation, the vehicle positions were compared with GPS information and superimposed onto aerial images collected by Google map API. The experimental results showed that the method based on EKF without using prior rotation information given error is about 1.9 times larger than our proposed method.en_US
dc.description.abstractIn recent years, classical structure from motion based SLAM has achieved significant results. Omnidirectional camera-based motion estimation has become interested researchers due to the lager field of view. This paper proposes a method to estimate the 2D motion of a vehicle and mapping by using EKF based on edge matching and one point RANSAC. Edge matching based azimuth rotation estimation is used as pseudo prior information for EKF predicting state vector. In order to reduce requirement parameters for motion estimation and reconstruction, the vehicle moves under nonholonomic constraints car-like structured motion model assumption. The experiments were carried out using an electric vehicle with an omnidirectional camera mounted on the roof. In order to evaluate the motion estimation, the vehicle positions were compared with GPS information and superimposed onto aerial images collected by Google map API. The experimental results showed that the method based on EKF without using prior rotation information given error is about 1.9 times larger than our proposed method.en_US
dc.language.isoengen_US
dc.rightshttps://creativecommons.org/licenses/by-nc-sa/4.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectOmnidirectional cameraen_US
dc.subjectedge feature matchingen_US
dc.subjectone-point RANSACen_US
dc.subjectmotion and mappingen_US
dc.titleCombining Edge and One-Point RANSAC Algorithm to Estimate Visual Odometryen_US
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record