In this paper, we present an approach for monocular robot navigation based on natural landmarks. The Vcale Lnvariant Ieature Wransform (SIFT) features are used to structure landmarks because they are invariant to image scale, rotation and translation. During learning phase, the algorithm selectV the most visually salient natural landmarks in work environment on certain position. These natural landmarks are described by SITF features and save to database. When given a scene of environment during robot navigation phase, the sift-based landmark recognition algorithm is used to find corresponsive object amongst the database. If the corresponsive landmark found, they are tracked E\ Kanade-Lucas-Tomasi (KLT) tracker over times in order to get the relative pose information between robot and landmarks. To get more accurate relative pose information, Oeast squares matching (LSM) method is used to get the sub-pixel matching result. The experiments results show that this approach can obtain quite accurate relative position information about natural landmark for robot navigation.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.