Abstract
A method for infrared and cameras sensor fusion, applied to indoor positioning in intelligent spaces, is proposed in this work. The fused position is obtained with a maximum likelihood estimator from infrared and camera independent observations. Specific models are proposed for variance propagation from infrared and camera observations (phase shifts and image respectively) to their respective position estimates and to the final fused estimation. Model simulations are compared with real measurements in a setup designed to validate the system. The difference between theoretical prediction and real measurements is between cm (fusion) and cm (camera), within a 95% confidence margin. The positioning precision is in the cm level (sub-cm level can be achieved at most tested positions) in a m locating cell with 5 infrared detectors on the ceiling and one single camera, at distances from target up to 5 m and 7 m respectively. Due to the low cost system design and the results observed, the system is expected to be feasible and scalable to large real spaces.
Highlights
The framework of this proposal is positioning in indoor Intelligent Spaces
One of them is closer to the theoretical description, based on unbiased Gaussian uncertainty of the observations and the other one consists in defining a new standard deviation upon the real measurements
The latter differs more from the theoretical assumptions but approaches better the real behavior and allows for having a practical design tool with the same theoretical basics derived in previous sections
Summary
Depending on the application goals, a rough, but useful, classification may divide the indoor positioning systems in non-precise systems (from some tens of cm to 1 m level) or precise ones (1 to 10 cm) The former are typically human-centered applications in which m-level or room-level accuracy may be enough to fulfill the requirements (for example, localization of people or objects in office buildings). They are usually user-oriented applications based on portable technologies as mobile phones [3], Inertial Measurement Units (IMUs), etc. They exploit the benefits of having an available infrastructure in the environment reducing, costs and sensor design effort at the expense of reaching lower precision than an ad-hoc sensorial positioning system, as the networks are originally conceived for communication
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