FastCal: Robust Online Self-calibration for Robotic Systems

Abstract

We propose a solution for sensor extrinsic self-calibration with 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 Fischer 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.