Map Building for a Service Mobile Robot Using Interactive GUI
ABSTRACT This paper presents a method of map building using interactive GUI for an indoor service mobile robot. The reason we proposed this method is that it is difficult for a mobile robot to generate an accurate map although many kinds of sensors are used. In proposed system, the operator can modify map built by LRF and odometry, compared with the real-time video from web camera using modification tool in developed interactive GUI. In order to improve self-localization of mobile robot, extended Kalman filter (EKF) was used. This paper introduces the architecture of the proposed system and gives some experimental results.
Conference Paper: Fuzzy approach for mobile robot positioning[Show abstract] [Hide abstract]
ABSTRACT: This paper presents a fuzzy approach for positioning an iRobot B21r mobile robot in an indoor environment. A novel error model for the laser rangefinder is built with consideration of the detection distance and the detection angle, and a new concept, the virtual angular point, is introduced as the feature for positioning the mobile robot in this paper. Such points as break points, real angular points, and virtual angular points are employed for positioning a mobile robot. Positions obtained by two arbitrary pairs of feature points are fused together by the weighted mean technique, and the weights are determined by the fuzzy accuracy of the feature points. Experimental study has been carried out to verify the effectiveness of the algorithms.Intelligent Robots and Systems, 2005. (IROS 2005). 2005 IEEE/RSJ International Conference on; 09/2005
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ABSTRACT: Research on mobile robot navigation has produced two major paradigms for mapping indoor environments: grid-based and topological. While grid-based methods produce accurate metric maps, their complexity often prohibits efficient planning and problem solving in large-scale indoor environments. Topological maps, on the other hand, can be used much more efficiently, yet accurate and consistent topological maps are considerably difficult to learn in large-scale environments. This paper describes an approach that integrates both paradigms: grid-based and topological. Grid-based maps are learned using artificial neural networks and Bayesian integration. Topological maps are generated on top of the grid-based maps, by partitioning the latter into coherent regions. By combining both paradigms---grid-based and topological---, the approach presented here gains the best of both worlds: accuracy/consistency and efficiency. The paper gives results for autonomously operating a mobile robot equipped w...
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ABSTRACT: This paper demonstrates a reliable navigation of a mobile robot in outdoor environment. We fuse differential GPS and odometry data using the framework of extended Kalman filter to localize a mobile robot. And also, we propose an algorithm to detect curbs through the laser range finder. An important feature of road environment is the existence of curbs. The mobile robot builds the map of the curbs of roads and the map is used for tracking and localization. The navigation system for the mobile robot consists of a mobile robot and a control station. The mobile robot sends the image data from a camera to the control station. The control station receives and displays the image data and the teleoperator commands the mobile robot based on the image data. Since the image data does not contain enough data for reliable navigation, a hybrid strategy for reliable mobile robot in outdoor environment is suggested. When the mobile robot is faced with unexpected obstacles or the situation that, if it follows the command, it can happen to collide, it sends a warning message to the teleoperator and changes the mode from teleoperated to autonomous to avoid the obstacles by itself. After avoiding the obstacles or the collision situation, the mode of the mobile robot is returned to teleoperated mode. We have been able to confirm that the appropriate change of navigation mode can help the teleoperator perform reliable navigation in outdoor environment through experiments in the road.2007 IEEE International Conference on Robotics and Automation, ICRA 2007, 10-14 April 2007, Roma, Italy; 01/2007