Speech Recognition
Speech Recognition Technologies Abstract While commercial solutions for precise indoor positioning exist, they are costly and require installation of additional infrastructure, which limits opportunities for widespread adoption. Inspired by robotics techniques of Simultaneous Localization and Mapping (SLAM) and computer vision approaches using structured light patterns, we propose a self-contained solution to precise indoor positioning that requires no additional environmental infrastructure. Evaluation of our prototype, called TrackSense, indicates that such a system can deliver up to 4 cm accuracy with 3 cm precision in rooms up to five meters squared, as well as 2 degree accuracy and 1 degree precision on orientation. We explain the design and performance characteristics of our prototype and demonstrate a feasible miniaturization that supports applications that require a single device localizing itself in a space. We also discuss extensions to locate multiple devices and limitations of this approach. 2. Introduction We introduce a solution to indoor localization, TrackSense, that requires no additional infrastructure in the environment and provides 3D positioning and orientation data that performs well against existing research and commercial solutions. Although we have seen great progress toward the goal of indoor localization, almost all of the solutions that offer precise (few centimeter) indoor localization have been limited to techniques that require the introduction of new infrastructure to the physical space (e.g. cameras or beacons). These solutions are often costly and typically require time-consuming installations, and it is not easy to move the instrumentation from one space to another. Although existing commercial positioning systems are adequate for prototyping user experiences, their ultimate success relies on a localization approach that is inexpensive and easily deployed. 3. Accuracy It is notoriously difficult to measure the accuracy of speech
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