Conservative Compression of Information Matrices using Event-Triggering and Robust Optimization
Christopher Funk, Benjamin Noack · 2024
Distributed sensor fusion requires the transmission of intermediate fusion results, consisting of point estimates and associated error covariance or information matrices. Bandwidth constraints necessitate data compression techniques for error covariance and information matrices, which typically dominate data volume. To ensure the safe use of the fusion results for decision-making, these techniques must be conservative, i.e., not lead to the compressed error covariance or information matrices underestimating the true estimate error. This work introduces a novel approach for the conservative compressed transmission of information matrices, that builds on a previous event-based method for covariance matrices. The proposed method allows the entire sensor fusion pipeline to operate in ‘information space’, facilitating efficient fusion operations without the need to compute corresponding covariance matrices. Contributions include an event-trigger for information matrices and a robust-optimization-based bounding mechanism ensuring conservativeness. The proposed approach is evaluated in the context of transmitting error information matrices generated by extended information filter SLAM to a receiver for further processing.