Distributed estimation for spatial rigid motion based on dual quaternions

This paper proposes a distributed optimization algorithm for 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 cooperation between connected sensors. The distributed Newton optimization method is 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. Simulation examples are demonstrated to compare the proposed algorithm with other methods in terms of estimation accuracy and converging rate.