FastCal: Robust Online Self-Calibration for Robotic Systems
This addresses the need for efficient and robust calibration in resource-constrained robotic systems, representing an incremental improvement over existing methods.
The paper tackles the problem of sensor extrinsic self-calibration in robotics by proposing FastCal, which achieves up to an order of magnitude faster runtime than similar algorithms while maintaining competitive accuracy and handling drift and unobservable directions.
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.