Modeling Error in Optical Angle Measurements for Position and Orientation Estimation in Sensor Networks - Vukasin, Gabrielle. (2017)
Abstract: In this dissertation, I describe two investigations involving the use of angle-of-arrival (AoA) measurements to enable decentralized localization within a robot team. The first investigation developed a new method for relative orientation estimation by modifying an existing relative-position algorithm called alternating-normals iterative method (ANIM). The second investigation was an experimental characterization of ANIM performance using camera sensors. This was the first time that ANIM has been tested in hardware, as the method has previously been implemented only in simulation. The major outcome of the first investigation is that with orientation estimation, the position error produced by the modified algorithm increased by a factor of two. The major outcome of the second investigation is the justification of an error model for the camera sensor as a Gaussian distribution of noise centered on the original AoA measurement with a standard deviation of 0.4 ± 0.1 degrees.; Thesis (M.S.)--Tufts University, 2016.; Submitted to the Dept. of Mechanical Engineering.; Advisors: Jason Rife, and Chris Rogers.; Committee: Ethan Danahy.; Keywords: Mechanical engineering, and Robotics.