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Reliably localizing and tracking displays moving relative to content in the physical world is one of the primary technical challenges facing all augmented reality systems. While significant progress has been made in recent years, all approaches remain limited to functioning only in certain environments and situations. Attempts to improve solution generality via additional sensors (e.g., depth sensors,...
Tele-robotic localization systems vary in implementation, but the cost of building such solutions is high. Therefore, utilizing such solutions in complex areas becomes a very difficult choice. Can we send a high-end costly solution to a hazardous or military location where it can be destroyed or lost? Or is it feasible to use such solution in routine work at home or in office for example? To answer...
This paper describes an implementation of autonomous navigation for a mobile robot in an indoor environment. Autonomous navigation requires map building, localization, path planning, obstacle detection, obstacle avoidance, path following, and so on. For the map building, ICP(Iterative Closest Point) builds floor map represented as occupancy grids. MCL(Monte Carlo Localization) updates robot location...
The Indoor Navigation Framework we have proposed allows any wheelchair user to be guided to a desired location on his own, as long as the building itself is adopted to the novel system. Unlike the state of the art, where no automation exists for guiding a wheelchair in modern buildings, this allows the owner of the least sophisticated wheelchair to explore public buildings with more ease than a normal...
Applications with street navigation have been recently introduced on mobile phone devices. A major part of existing systems use integrated GPS as input for indicating the location. However, these systems often fail or make abrupt shifts in urban environment due to occlusion of satellites. Furthermore, they only give the position of a person and not the object of his attention, which is just as important...
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...
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