Monocular robot navigation using invariant natural features

Abstract

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.

Unsupervised place recognition for assistive mobile robots based on local feature descriptions

“…In such cases, they work as a hidden layer. For instance, an approach for monocular robot navigation based on natural landmarks was presented in [23]. The natural landmarks are extracted by a scale invariant feature transform (SIFT) [17] and stored in a database.…”