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An increasing number of simultaneous localization and mapping (SLAM) systems are using appearance-based localization to improve the quality of pose estimates. However, with the growing time-spans and size of the areas we want to cover, appearance-based maps are often becoming too large to handle and are consisting of features that are not always reliable for localization purposes. This paper presents...
Estimating the geographical position of mobile device such as Smart phone in an indoor environment is not easy without the use of specific infrastructures. In this article, we introduce an indoor user tracking system. The system constructs a topological map with Wi-Fi signal calibrations, assigns semantically meaningful labels into the map, and estimates the semantic location of the user based on...
Simultaneous Localization and Mapping (SLAM) addresses the problem of a robot navigating and acquiring spatial models of initially unknown environments, without an absolute localization means. To solve this problem, we propose a mapping system that builds feature-based geometrical maps by applying a modified Particle Swarm Optimization (PSO) algorithm. Particles are defined as the location of individual...
The GPS has revolutionized how people, vehicles, and objects are positioned. The GPS, however, has limitations. It will only work well where a signal can be received and will not work underground, in tunnels, or even some buildings. Obtaining an accurate position estimate in these areas must therefore use alternate methods that do not rely on GPS. Promising research from the field of robotics provides...
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