MASAT: A fast and robust algorithm for pose-graph initialization

•A low-complexity, computationally fast initialization algorithm (MASAT) is described for 2D and 3D pose-graph optimizations.•MASAT outperforms other lightweight baseline initialization algorithms.•MASAT outperforms complex initialization algorithms in case of 3D datasets.•An extensive evaluation and comparison is reported using three 2D and three 3D datasets.•The source code of the proposed algorithm, and all the data used for evaluation are provided. In this paper, we propose a novel algorithm to compute the initial structure of pose-graph based Simultaneous Localization and Mapping (SLAM) systems. We perform a Breadth-First Search (BFS) on the graph in order to obtain multiple votes regarding the location of a certain robot position from all of its previously processed neighbors. Next, we define the initial location of a pose as the average of the multiple alternatives. By adopting the proposed initialization approach, the number of iterations needed for optimization is significantly reduced while the computational complexity remains lightweight. We perform quantitative evaluation on various 2D and 3D benchmark datasets to demonstrate the advantages of the proposed method.