  This paper introduces a novel and distributed method for detecting inter-map
loop closure outliers in simultaneous localization and mapping (SLAM). The
proposed algorithm does not rely on a good initialization and can handle more
than two maps at a time. In multi-robot SLAM applications, maps made by
different agents have nonidentical spatial frames of reference which makes
initialization very difficult in the presence of outliers. This paper presents
a probabilistic approach for detecting incorrect orientation measurements prior
to pose graph optimization by checking the geometric consistency of rotation
measurements. Expectation-Maximization is used to fine-tune the model
parameters. As ancillary contributions, a new approximate discrete inference
procedure is presented which uses evidence on loops in a graph and is based on
optimization (Alternate Direction Method of Multipliers). This method yields
superior results compared to Belief Propagation and has convergence guarantees.
Simulation and experimental results are presented that evaluate the performance
of the outlier detection method and the inference algorithm on synthetic and
real-world data.
