FastCal: Robust Online Self-Calibration for Robotic Systems

TL;DR

FastCal is a fast, robust online self-calibration method for robotic sensors that handles drift and unobservable parameters efficiently, suitable for resource-constrained systems.

Contribution

It introduces a low-complexity, observability-aware self-calibration algorithm with information-theoretic segment selection and drift correction, outperforming existing methods in speed.

Findings

Runs up to ten times faster than similar algorithms

Handles calibration drift and unobservable directions effectively

Suitable for resource-constrained robotic systems

Abstract

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.