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Large-scale model-assisted bundle adjustment using Gaussian max-mixtures

This paper reports on a model-assisted bundle adjustment framework in which visually-derived features are fused with an underlying three-dimensional (3D) mesh provided a priori. By using an approach inspired by the expectation-maximization (EM) class of algorithms, we introduce a hidden binary label for each visual feature that indicates if that feature is considered part of the nominal model, or if the feature corresponds to 3D structure that is absent from this model. Therefore, in addition to improved estimates of the feature locations, we can also label the features based on their deviation from the model. We show that this method is a special case of the Gaussian max-mixtures framework, which can be efficiently incorporated into state-of-the-art graph-based simultaneous localization and mapping (SLAM) solvers. We provide field tests taken from the Bluefin Robotics Hovering Autonomous Underwater Vehicle (HAUV) surveying the SS Curtiss.