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In this work, we present an approach for monocular hand-eye calibration from per-sensor ego-motion based on dual quaternions. Due to non-metrically scaled translations of monocular odometry, a scaling factor has to be estimated in addition to the rotation and translation calibration. For this, we derive a quadratically constrained quadratic program that allows a combined estimation of all extrinsic calibration parameters. Using dual quaternions leads to low run-times due to their compact representation. Our problem formulation further allows to estimate multiple scalings simultaneously for different sequences of the same sensor setup. Based on our problem formulation, we derive both, a fast local and a globally optimal solving approach. Finally, our algorithms are evaluated and compared to state-of-theart approaches on simulated and real-world data, e.g., the EuRoC MAV dataset.

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We previously presented the product multi-sensor generalized labeled multi-Bernoulli filter, which constitutes a multi-object filter for centralized and distributed multi-sensor systems with centralized estimator. It implements the Bayes parallel combination rule for generalized labeled multi-Bernoulli densities, simplifying the NP-hard multidimensional k-best assignment problem of the multi-sensor multi-object update to a polynomial-time k-shortest path problem. This way, the filter allows for efficient, parallelizable, and distributed calculation of the multi-sensor multi-object update, while showing excellent performance. However, the derivation of the filter formulas relies on a well-established approximation of the fundamental multisensor Gaussian identity, which was inadvertently not labeled as such in our original article. Thus, on the one hand, we clarify this mistake, discuss its consequences, and present a mathematically clean derivation of the filter yet to establish the claim of Bayesoptimality. On the other hand, we discuss implementation details and present extensive evaluations, that complete the previous publication of the filter.