Abstract
The creation of large photogrammetric models often encounter several difficulties in regards to geometric accuracy, scale and geolocation, especially when not using control points. Geometric accuracy can be a problem when encountering repetitive features, scale and geolocation can be challenging in GNSS denied or difficult to reach environments. Despite these challenges scale and location are often highly desirable even if only approximate, especially when the error bounds are known. Using non-parametric belief propagation we propose a method of fusing different sensor types to allow robust creation of scaled models without control points. Using this technique we scale models using only the sensor data sometimes to within 4% of their actual size even in the presence of poor GNSS coverage.
Highlights
As the number and quality of sensors increases the need to have a method of fusing this information increases
This research has resulted in several different forms of sensor fusion each with its own limitations
In this paper we present a method of fusing accelerometer, barometric and GNSS measurements along with images using a nonparametric belief propagation and photogrammetry
Summary
As the number and quality of sensors increases the need to have a method of fusing this information increases. For instance Bayesian based methods are popular, need to have the error distributions of the sensors specified a priori (Koks and Challa, 2003). When compared to sensors designed for navigation their accuracy is lacking. Despite their lower accuracy these sensors still find uses in many applications such as orientation finding (Ayub et al, 2012), activity identification (Tundo et al, 2013) and geotagging for Structure from Motion (Crandall et al, 2013)
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