Map-aided 6-DOF relative pose estimation for monocular SLAM using sparse information filters

Wang, Z; Dissanayake, G

Map-aided 6-DOF relative pose estimation for monocular SLAM using sparse information filters

Wang, Z Dissanayake, G

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Publication Type:: Conference Proceeding
Citation:: 11th International Conference on Control, Automation, Robotics and Vision, ICARCV 2010, 2010, pp. 1006 - 1011
Issue Date:: 2010-12-01

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Field	Value	Language
dc.contributor.author	Wang, Z	en_US
dc.contributor.author	Dissanayake, G https://orcid.org/0000-0002-7992-0680	en_US
dc.date.issued	2010-12-01	en_US
dc.identifier.citation	11th International Conference on Control, Automation, Robotics and Vision, ICARCV 2010, 2010, pp. 1006 - 1011	en_US
dc.identifier.isbn	9781424478132	en_US
dc.identifier.uri	http://hdl.handle.net/10453/16451
dc.description.abstract	This paper addresses the problem of mapping three dimensional environments from a sequence of images taken by a calibrated camera, and simultaneously generating the camera motion trajectory. This is the Monocular SLAM problem in robotics, and is akin to the Structure from Motion (SFM) problem in computer vision. We present a novel map-aided 6-DOF relative pose estimation method based on a new formulation of the Monocular SLAM that is able to provide better initial estimates of new camera poses than the simple triangulation traditionally used in this context. The '6-DOF' means relative to the map which itself is up to an unobservable scale. The proposed pose estimator also allows more effective outlier rejection in matching features present in the map and features extracted from two consecutive images. Our Monocular SLAM algorithm is able to deal with arbitrary camera motion, making the smooth motion assumption, which is required by the typically used constant velocity model, unnecessary. In the new Monocular SLAM formulation, the measurements of extracted features from images are partitioned into those used for the estimation of the environment and those used for estimating the camera motion. The new formulation enables the current map estimate to aid achieving the full 6-DOF relative pose estimation up to the mapping scale while maximally exploiting the geometry information in images. Experiment results are provided to verify the proposed algorithm. ©2010 IEEE.	en_US
dc.relation.ispartof	11th International Conference on Control, Automation, Robotics and Vision, ICARCV 2010	en_US
dc.relation.isbasedon	10.1109/ICARCV.2010.5707884	en_US
dc.title	Map-aided 6-DOF relative pose estimation for monocular SLAM using sparse information filters	en_US
dc.type	Conference Proceeding
utslib.for	090602 Control Systems, Robotics and Automation	en_US
dc.location.activity	Singapore	en_US
dc.location.activity	Singapore, SINGAPORE
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CAS - Centre for Autonomous Systems
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

This paper addresses the problem of mapping three dimensional environments from a sequence of images taken by a calibrated camera, and simultaneously generating the camera motion trajectory. This is the Monocular SLAM problem in robotics, and is akin to the Structure from Motion (SFM) problem in computer vision. We present a novel map-aided 6-DOF relative pose estimation method based on a new formulation of the Monocular SLAM that is able to provide better initial estimates of new camera poses than the simple triangulation traditionally used in this context. The '6-DOF' means relative to the map which itself is up to an unobservable scale. The proposed pose estimator also allows more effective outlier rejection in matching features present in the map and features extracted from two consecutive images. Our Monocular SLAM algorithm is able to deal with arbitrary camera motion, making the smooth motion assumption, which is required by the typically used constant velocity model, unnecessary. In the new Monocular SLAM formulation, the measurements of extracted features from images are partitioned into those used for the estimation of the environment and those used for estimating the camera motion. The new formulation enables the current map estimate to aid achieving the full 6-DOF relative pose estimation up to the mapping scale while maximally exploiting the geometry information in images. Experiment results are provided to verify the proposed algorithm. ©2010 IEEE.

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