Research Article
Locus: An Indoor Localization, Tracking and Navigation System for Multi-story Buildings Using Heuristics Derived from Wi-Fi Signal Strength
@INPROCEEDINGS{10.1007/978-3-642-40238-8_18, author={Preeti Bhargava and Shivsubramani Krishnamoorthy and Aditya Nakshathri and Matthew Mah and Ashok Agrawala}, title={Locus: An Indoor Localization, Tracking and Navigation System for Multi-story Buildings Using Heuristics Derived from Wi-Fi Signal Strength}, proceedings={Mobile and Ubiquitous Systems: Computing, Networking, and Services. 9th International Conference, MobiQuitous 2012, Beijing, China, December 12-14, 2012. Revised Selected Papers}, proceedings_a={MOBIQUITOUS}, year={2013}, month={9}, keywords={Indoor location Localization Tracking Navigation Context- and location-aware applications and services}, doi={10.1007/978-3-642-40238-8_18} }- Preeti Bhargava
Shivsubramani Krishnamoorthy
Aditya Nakshathri
Matthew Mah
Ashok Agrawala
Year: 2013
Locus: An Indoor Localization, Tracking and Navigation System for Multi-story Buildings Using Heuristics Derived from Wi-Fi Signal Strength
MOBIQUITOUS
Springer
DOI: 10.1007/978-3-642-40238-8_18
Abstract
The holy grail in indoor location technology is to achieve the milestone of combining minimal cost with accuracy, for general consumer applications. A low-cost system should be inexpensive both to install and maintain, requiring only available consumer hardware to operate and its accuracy should be room-level or better. To achieve this, current systems require either extensive calibration or expensive hardware. Moreover, very few systems built so far have addressed localization in multi-story buildings. We explain a heuristics based indoor localization, tracking and navigation system for multi-story buildings called Locus that determines floor and location by using the locations of infrastructure points, and without the need for radio maps or calibration. It is an inexpensive solution with minimum setup and maintenance expenses. Initial experimental results in an indoor space spanning 175,000 square feet, show that it can determine the floor with 99.97% accuracy and the location with an average location error of 7m.
