Distributed estimation for spatial rigid motion based on dual quaternions

This paper proposes 2 distributed optimization algorithms for the estimation of spatial rigid motion using multiple image sensors in a connected network. The objective is to increase the estimation precision of translational and rotational motion based on dual quaternion models and the cooperation between connected sensors. The dual decomposition subgradient method and distributed Newton optimization method are applied to decompose the filtering task into a series of suboptimal problems and then solve them individually to achieve the global optimality. Our approach assumes that each sensor can communicate with its neighboring sensors to update the individual estimates. Discussion on converging speed of both methods are provided. Simulation examples are demonstrated to compare the 2 distributed algorithms with the traditional extended Kalman filter in terms of estimation accuracy and converging rate.