# 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.

## Key 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.

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Source: https://tomesphere.com/paper/1902.10585