A model-based method for indoor mobile robot localization using monocular vision and straight-line correspondences

Abstract

International audienceA model-based method for indoor mobile robot localization is presented herein; this method relies on monocular vision and uses straight-line correspondences. A classical four-step approach has been adopted (i.e. image acquisition, image feature extraction, image and model feature matching, and camera pose computing). These four steps will be discussed with special focus placed on the critical matching problem. An efficient and simple method for searching image and model feature correspondences, which has been designed for indoor mobile robot self-location, will be highlighted: this is a three-stage method based on the interpretation tree search approach. During the first stage, the correspondence space is reduced by virtue of splitting the navigable space into view-invariant regions. In making use of the specificity of the mobile robotics frame of reference, the global interpretation tree is divided into two sub-trees; two low-order geometric constraints are then defined and applied directly on 2D3D correspondences in order to improve pruning and search efficiency. During the last stage, the pose is calculated for each matching hypothesis and the best one is selected according to a defined error function. Test results illustrate the performance of this approach

A Review of Indoor Localization Technologies: towards Navigational Assistance for Topographical Disorientation

“…The articles selected for this section can be distinguished based on the sensor required to estimate the location of the tracked object. Some articles proposed the use of cameras to locate a mobile subject or device via image processing [58,5,94,24,21,93,97,89,39,70,31,48,10]. In contrast, some other articles present localization solutions based on non-image processing devices [15,56,55,14,33,90].…”

Implementation of vision based 2-DOF underwater Manipulator

“…Based on the specific needs of the subject, the object is a red ball of a specific size and shape. [7] Color model can be divided into several categories, such as RGB, YUV, HIS and CIELAB. Different color models have different characteristics and application fields, and no color model can meet all the requirements of users [8].This paper proposes a method which uses two different color models (RGB and HSI).This method can take advantages of two color models, so as to get higher accuracy of image segmentation results.…”

Model based Kalman Filter Mobile Robot Self-Localization

“…This rises a matching problem if one model line has more then one image candidate, but the problem was solved in Kosaka & Kak (1992) and Aider et al (2005). There are also quantization error, noise in camera image and error in edge detection and image segmentation which have been approximated by Gaussian variable ξ ∼ N (0, V) and included in the EKF equations as the measurement noise.…”

“…Guilherme & Avinash (2002), but for reasons of size and cost monocular vision based navigation has been addressed by a number of researchers, e.g. Aider et al (2005); Jeon & Kim (1999); Kosaka & Kak (1992); Neira et al (1997). When using monocular vision, the localization process is composed of the five following stages Guilherme & Avinash (2002); Kosaka & Kak (1992): 1) image acquisition from current robot pose; 2) image segmentation and feature extraction; 3) model rendering; 4) matching 2D-image and 3D-model features and 5) camera pose computation.…”

“…The strategy ultimately adopted for each phase must then be very well-assessed for real-time use. For example, an efficient real-time solution to the feature matching problem is presented in Aider et al (2005), where interpretation tree search techniques were applied. For mobile robot working environment modelling and rendering professional freeware computer graphics tool Blender www.blender3d.org (1995) was used, which is an open source software for 3D modelling, animation, rendering, post-production, interactive creation and playback.…”