  We propose a solution for sensor extrinsic self-calibration with very low
time complexity, competitive accuracy and graceful handling of often-avoided
corner cases: drift in calibration parameters and unobservable directions in
the parameter space. It consists of three main parts: 1) information-theoretic
based segment selection for constant-time estimation; 2) observability-aware
parameter update through a rank-revealing decomposition of the Fisher
information matrix; 3) drift-correcting self-calibration through the time-decay
of segments. At the core of our FastCal algorithm is the loosely-coupled
formulation for sensor extrinsics calibration and efficient selection of
measurements. FastCal runs up to an order of magnitude faster than similar
self-calibration algorithms (camera-to-camera extrinsics, excluding
feature-matching and image pre-processing on all comparisons), making FastCal
ideal for integration into existing, resource-constrained, robotics systems.
