Distributed simultaneous localization and mapping for mobile robot networks via hybrid dynamic belief propagation

Abstract

This article proposes a hybrid dynamic belief propagation for simultaneous localization and mapping in the mobile robot network. The positions of landmarks and the poses of moving robots at each time slot are estimated simultaneously in an online and distributed manner, by fusing the odometry data of each robot and the measurements of robot-robot or robot-landmark relative distance and angle. The joint belief state of all robots and landmarks is encoded by a factor graph and the marginal posterior probability distribution of each variable is inferred by belief propagation. We show how to calculate, broadcast, and update messages between neighboring nodes in the factor graph. Specifically, we combine parametric and nonparametric techniques to tackle the problem arisen from non-Gaussian distributions and nonlinear models. Simulation and experimental results on publicly available dataset show the validity of our algorithm.

A Robot Web for Distributed Many-Device Localisation

“…Then Caceres et al [7] extended [44] to a network composed of GNSS nodes. These distributed positioning methods were later generalized by adding nonlinear measurement models and utilizing Gaussian message passing [33], [34]; and in 2017, Wan et al [43] proposed a distributed multi-robot SLAM algorithm, using belief propagation. In their method, a mixture of Gaussian and non-parametric models was used to handle nonlinear models.…”

A Moving Target Online Location Algorithm Based on Factor Graph

“…In the literature [19], Tang et al proposed a colocation algorithm for distributed underwater node weighted factor graphs, i.e., using factor graphs and sum-product algorithms to decompose global optimization into the product of several local optimization functions, but the complexity of algorithm has a certain improvement. In literature the [20], Wan et al simultaneously estimate the landmark position and attitude of mobile robots online and distributed by combining range data from each robot with the relative robot-robot or robot landmark distances and angles. Its main focus is on non-Gaussian distributions.…”