Statistical Outlier Identification in Multi-robot Visual SLAM using Expectation Maximization
This addresses the challenge of outlier detection in multi-robot SLAM, which is crucial for accurate map alignment but is incremental as it builds on existing probabilistic and optimization techniques.
The paper tackles the problem of detecting inter-map loop closure outliers in multi-robot visual SLAM by introducing a probabilistic method that checks geometric consistency of rotation measurements, using Expectation-Maximization for parameter tuning, and reports superior results compared to Belief Propagation with convergence guarantees.
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.