In this paper we present a visiorz-based approuch to n~o -bile robot localization, that integrates an inzage retrieval system with Monte-Carlo loculization. The iniage retrieval process is based on features that are invariant with respect to image iranslations, rotations, and limited scale. Since it furthennore uses local features, the system is robust against distortion and occlusions which is especially inzportant in populated environments. By using the saniple-based Monte-Carlo localization technique our robot is able to globally localize itselJ; to reliably keep track of its position, and to recover from localization failures. Both techniques are combined by extracting for each iinage a set ofpossible view-points using a two-diniensional map of the em~ironment. Our technique has been iinpleniented and tested extensively. We present several experiments denionstraring the reliability and robustiless qf our approach even in the context of dynaniics in the environntent and larger errors in the odonietiy
In REF , Wolf et al. use local scale-invariant features and combine with Monte-Carlo localization to estimate robot positions.
Robust vision-based localization for mobile robots using an image retrieval system based on invariant features
Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292)
Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292)
Computer Science