An Introduction to Variational Methods for Graphical Models
Machine Learning
Using covariance intersection for SLAM
Robotics and Autonomous Systems
Walk-Sums and Belief Propagation in Gaussian Graphical Models
The Journal of Machine Learning Research
Distributed maximum a posteriori estimation for multi-robot cooperative localization
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Consistent cooperative localization
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Distributed collaborative localization of multiple vehicles from relative pose measurements
Allerton'09 Proceedings of the 47th annual Allerton conference on Communication, control, and computing
Distributed vision-aided cooperative localization and navigation based on three-view geometry
AERO '11 Proceedings of the 2011 IEEE Aerospace Conference
IEEE Transactions on Robotics
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Cooperative navigation (CN) enables a group of cooperative robots to reduce their individual navigation errors. For a general multi-robot (MR) measurement model that involves both inertial navigation data and other onboard sensor readings, taken at different time instances, the various sources of information become correlated. Thus, this correlation should be solved for in the process of information fusion to obtain consistent state estimation. The common approach for obtaining the correlation terms is to maintain an augmented covariance matrix. This method would work for relative pose measurements, but is impractical for a general MR measurement model, because the identities of the robots involved in generating the measurements, as well as the measurement time instances, are unknown a priori. In the current work, a new consistent information fusion method for a general MR measurement model is developed. The proposed approach relies on graph theory. It enables explicit on-demand calculation of the required correlation terms. The graph is locally maintained by every robot in the group, representing all of the MR measurement updates. The developed method calculates the correlation terms in the most general scenarios of MR measurements while properly handling the involved process and measurement noise. A theoretical example and a statistical study are provided, demonstrating the performance of the method for vision-aided navigation based on a three-view measurement model. The method is compared, in a simulated environment, with a fixed-lag centralized smoothing approach. The method is also validated in an experiment that involved real imagery and navigation data. Computational complexity estimates show that the newly developed method is computationally efficient.