Unsupervised Tuning of Filter Parameters Without Ground-Truth Applied to Aerial Robots

Autonomous robots heavily rely on well-tuned state estimation filters for successful control. This letter presents a novel automatic tuning strategy for learning filter parameters by minimizing the innovation, i.e., the discrepancy between expected and received signals from all sensors. The optimization process only requires the inputs and...