Robust Multi-robot Map Merging Based on Continuous-Discrete MRF Graph Model

In the multi-robot simultaneous localization and mapping (MSLAM), perceptual aliasing will generate highly correlated and mutual-consistent spurious inter-map loop closures, which will lead to the failure of location and map deformations. A general continuous-discrete graph model based on Markov random field (MRF) is proposed to solve this problem. The continuous graph models the standard pose graph, and the discrete graph establishes the rejection model by explicit modeling of the correlation of outliers. On this basis, the convex relaxation method is further used to transform the non-convex and NP (non-deterministic polynomial) complete combinatorial optimization problem represented by the continuous-discrete graph into a semidefinite programming (SDP) problem, which is convenient to solve by using off-the-shelf convex solvers. Simulation and experiment results demonstrate that the proposed method can improve the robustness of the pose graph to the outliers caused by perceptual aliasing, and the result doesn't depend on the initial value of the pose. In the case of an outlier ratio of 50%, the rejection rate is still up to 99.8%, and the map fusion accuracy is better than the existing state-of-the-art DCS (dynamic covariance scaling) and PCM (pairwise consistent measurement) methods.