# Robust Intrinsic and Extrinsic Calibration of RGB-D Cameras

## TL;DR

This paper introduces a new calibration framework for RGB-D cameras that improves accuracy, is easy to use, and works across different sensor types, enhancing robotics applications like mapping and object recognition.

## Contribution

A novel two-component error model and calibration protocol that unifies error sources and improves calibration accuracy for various RGB-D sensors.

## Key findings

- Enhanced calibration accuracy over existing methods
- Applicable to different RGB-D sensor technologies
- Validated through extensive experiments and comparisons

## Abstract

Color-depth cameras (RGB-D cameras) have become the primary sensors in most robotics systems, from service robotics to industrial robotics applications. Typical consumer-grade RGB-D cameras are provided with a coarse intrinsic and extrinsic calibration that generally does not meet the accuracy requirements needed by many robotics applications (e.g., highly accurate 3D environment reconstruction and mapping, high precision object recognition and localization, ...). In this paper, we propose a human-friendly, reliable and accurate calibration framework that enables to easily estimate both the intrinsic and extrinsic parameters of a general color-depth sensor couple. Our approach is based on a novel two components error model. This model unifies the error sources of RGB-D pairs based on different technologies, such as structured-light 3D cameras and time-of-flight cameras. Our method provides some important advantages compared to other state-of-the-art systems: it is general (i.e., well suited for different types of sensors), based on an easy and stable calibration protocol, provides a greater calibration accuracy, and has been implemented within the ROS robotics framework. We report detailed experimental validations and performance comparisons to support our statements.