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		<doi>10.1109/SIBGRAPI.2017.7</doi>
		<citationkey>PereiraNegLufIlhSus:2017:MoViOd</citationkey>
		<title>Monocular Visual Odometry With Cyclic Estimation</title>
		<format>On-line</format>
		<year>2017</year>
		<numberoffiles>1</numberoffiles>
		<size>803 KiB</size>
		<author>Pereira, Fabio Irigon,</author>
		<author>Negreiros, Marcelo,</author>
		<author>Luft, Joel,</author>
		<author>Ilha, Gustavo,</author>
		<author>Susin, Altamiro,</author>
		<affiliation>PGMICRO-UFRGS</affiliation>
		<affiliation>DELET-UFRGS</affiliation>
		<affiliation>PPGEE-UFRGS</affiliation>
		<affiliation>PPGEE UFRGS</affiliation>
		<affiliation>PGMICRO-UFRGS</affiliation>
		<editor>Torchelsen, Rafael Piccin,</editor>
		<editor>Nascimento, Erickson Rangel do,</editor>
		<editor>Panozzo, Daniele,</editor>
		<editor>Liu, Zicheng,</editor>
		<editor>Farias, Mylène,</editor>
		<editor>Viera, Thales,</editor>
		<editor>Sacht, Leonardo,</editor>
		<editor>Ferreira, Nivan,</editor>
		<editor>Comba, João Luiz Dihl,</editor>
		<editor>Hirata, Nina,</editor>
		<editor>Schiavon Porto, Marcelo,</editor>
		<editor>Vital, Creto,</editor>
		<editor>Pagot, Christian Azambuja,</editor>
		<editor>Petronetto, Fabiano,</editor>
		<editor>Clua, Esteban,</editor>
		<editor>Cardeal, Flávio,</editor>
		<e-mailaddress>fabio.irigon@gmail.com</e-mailaddress>
		<conferencename>Conference on Graphics, Patterns and Images, 30 (SIBGRAPI)</conferencename>
		<conferencelocation>Niterói, RJ, Brazil</conferencelocation>
		<date>17-20 Oct. 2017</date>
		<publisher>IEEE Computer Society</publisher>
		<publisheraddress>Los Alamitos</publisheraddress>
		<booktitle>Proceedings</booktitle>
		<tertiarytype>Full Paper</tertiarytype>
		<transferableflag>1</transferableflag>
		<versiontype>finaldraft</versiontype>
		<keywords>Visual Odometry, Visual SLAM, Computer Vision.</keywords>
		<abstract>Monocular Visual Odometry (MVO) estimates the camera position and orientation, based on images generated by a single camera. In this paper a new sparse MVO system for camera equipped vehicles is proposed. Three view cyclic Perspective-n-Point with adaptive threshold is used for camera pose estimation, perspective image transformations are used to improve tracking, and a multi-attribute cost function selects ground features for scale recovery. Results using the KITTI dataset show that the proposed system achieves 1.29\% average translation error and average rotation precision of 0.0029 degrees per meter.</abstract>
		<language>en</language>
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		<usergroup>fabio.irigon@gmail.com</usergroup>
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