This paper presents a method of autonomous topological modeling and localization in a home environment using only low-cost sonar sensors. The topological model is extracted from a grid map using cell decomposition and normalized graph cut. The autonomous topological modeling involves the incremental extraction of a subregion without predefining the number of subregions. A method of topological localization based on this topological model is proposed wherein a current local grid map is compared with the original grid map. The localization is accomplished by obtaining a node probability from a relative motion model and rotational invariant grid-map matching. The proposed method extracts a well-structured topological model of the environment, and the localization provides reliable node probability even when presented with sparse and uncertain sonar data. Experimental results demonstrate the performance of the proposed topological modeling and localization in a real home environment.

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We propose a robust simultaneous localization and mapping (SLAM) with a Rao-Blackwellized particle filter (RBPF) algorithm for mobile robots using sonar sensors in non-static environments. The algorithm consists of three parts: sampling from multiple ancestor sets, estimating intermediate paths for map updates and eliminating spurious landmarks using negative information from sonar sensors. The proposed sampling method, in which particles are sampled from multiple ancestor sets, increases the robustness of the estimation of the robot's pose, even if environmental changes corrupt observations. This step increases the probability of some particles being sampled from correct ancestor sets that are updated by observations reflected from stationary objects. When particles are sampled from several time steps earlier, however, observations at intermediate time steps cannot be used to update the map because of the lack of information about the intermediate path. To update the map with all sensor information, the intermediate path is estimated after particles are sampled from ancestor sets. Finally, spurious landmarks still exist on the map representing objects that were eliminated or that were extracted by error in cluttered areas. These are eliminated in the final step using negative information from the sonar sensors. The performance of the proposed SLAM algorithm was verified through simulations and experiments in various non-static environments.

Purpose: We propose a localization algorithm for topological maps constituted by nodes and edges in a graph form. Our focus is to develop a robust localization algorithm that works well even under various dynamic noises.  Design/Methodology/Approach: For robust localization, we propose an algorithm which utilizes all available data such as node information, sensor measurements at the current time step (which are used in previous algorithms) and edge information, and sensor measurements at previous time steps (which have not been considered in other papers). Also, the algorithm estimates a robot's location in a multi-modal manner which increases its robustness. Findings: We show that the proposed algorithm works well in topological maps with various dynamics which are induced by the moving objects in the map and measurement noises from cheap sensors. Originality/value of the paper: Unlike previous approaches, the proposed algorithm has three key features: 1) usage of edge data, 2) inclusion of history information, and 3) a multi-modal based approach. By virtue of these features, we have developed an algorithm that enables robust localization performance.

Purpose-The purpose of this paper is to describe research to enable a robust navigation of guide robots in erratic environments with partial sensor information. Design/methodology/approach-Two techniques were developed. One is a robust node discrimination method by using an adaptive sensor matching method. The other is a robot navigation technique with partial sensor information. Findings-A successful navigation was implemented in erratic environments using partial sensor information. Originality/value-First robot navigation is addressed along the generalized Voronoi graph (GVG) with partial sensor information. A solution is also provided for a phantom node detection problem, which is one of the main defects in GVG navigation.