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%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi/2017/08.18.20.42
%2 sid.inpe.br/sibgrapi/2017/08.18.20.42.29
%T Monocular Visual Odometry With Cyclic Estimation
%D 2017
%8 Oct. 17-20, 2017
%A Pereira, Fabio Irigon,
%A Negreiros, Marcelo,
%A Luft, Joel,
%A Ilha, Gustavo,
%A Susin, Altamiro,
%@affiliation PGMICRO-UFRGS
%@affiliation DELET-UFRGS
%@affiliation PPGEE-UFRGS
%@affiliation PPGEE UFRGS
%@affiliation PGMICRO-UFRGS
%E Torchelsen, Rafael Piccin,
%E Nascimento, Erickson Rangel do,
%E Panozzo, Daniele,
%E Liu, Zicheng,
%E Farias, Mylène,
%E Viera, Thales,
%E Sacht, Leonardo,
%E Ferreira, Nivan,
%E Comba, João Luiz Dihl,
%E Hirata, Nina,
%E Schiavon Porto, Marcelo,
%E Vital, Creto,
%E Pagot, Christian Azambuja,
%E Petronetto, Fabiano,
%E Clua, Esteban,
%E Cardeal, Flávio,
%B Conference on Graphics, Patterns and Images, 30 (SIBGRAPI)
%C Niterói, RJ
%S Proceedings
%I IEEE Computer Society
%J Los Alamitos
%K Visual Odometry, Visual SLAM, Computer Vision.
%X 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.
%@language en
%3 PID4957061.pdf


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