In this paper, a new method of information fusion – DSmT (Dezert and Smarandache Theory) is introduced to apply to managing and dealing with the uncertain information from robot map building. Here we build grid map form sonar sensors and laser range finder (LRF). The uncertainty mainly comes from sonar sensors and LRF. Aiming to the uncertainty in static environment, we propose Classic DSm (DSm...

This work proposed a real-time 3D map building system based on the visual odometry information derived from a Multi-Camera hardware. The accurate odometry information and images derived from the proposed intuitive Multi-Camera hardware, so called Multi-Camera Unit (MCU), are used as an input for the 3D map building process which cooperates the FastSLAM algorithm as a map and path estimator. Exp...

Indoor maps are highly essential for indoor positioning and location-based services. People existing in big building such as mall, airport and hospitals leads to rapid growth in location based applications. Todays, typical indoor positioning systems employ a training/positioning model using Wi-Fi fingerprints. While these approaches have practical results in terms of coverage, they require an i...

In this paper we present a method which progresses towards automatically generating the texture maps for the building façades of an existing coarse three dimensional urban model. Rather than focusing on a method for achieving the exact texture map the algorithm creates two texture maps. These represent the building façade’s wall material and building features, such as windows and doors. The pap...

Exploration involving mapping and concurrent localization in an unknown environment is a pervasive task in mobile robotics. In general, the accuracy of the mapping process depends directly on the accuracy of the localization process. This paper address the problem of maximizing the accuracy of the map building process during exploration by adaptively selecting control actions that maximize loca...

This paper describes a new algorithm for autonomous mobile robot (AMR) map building and path planning in an unknown environment The robot learns its environment using a view-based probabilistic interpretation of the visible corner and edge features. No attempt is made to locate physical world features in a traditional Euclidean coordinate system, rather the algorithm produces map building throu...

In this article, we propose a new approach for mobile robot localization and hybrid map building simultaneously without using any odometry hardware system. The proposed method termed as Genetic Bayesian ARAM comprises twomain components: (1) steadystate genetic algorithm (SSGA) for self-localization and occupancy grid map building and (2) Bayesian Adaptive Resonance Associative Memory (ARAM) fo...