Online self-calibration for robotic systems.

We present a generic algorithm for self-calibration of robotic systems that utilizes two key innovations. First, it uses an information-theoretic measure to automatically identify and store novel measurement sequences. This keeps the computation tractable by discarding redundant information and allows the system to build a sparse but complete calibration dataset from data collected at different times. Second, as the full observability of the calibration parameters may not be guaranteed for an arbitrary measurement sequence, the algorithm detects and locks unobservable directions in parameter space using a combination of rank-revealing QR and singular value decompositions of the Fisher information matrix. The result is an algorithm that listens to an incoming sensor stream, builds a minimal set of data for estimating the calibration parameters, and updates parameters as they become observable, leaving the others locked at their initial guess. We validate our approach through an extensive set of simulated and real-world experiments.