RGB-Dカメラのロバストな内部・外部キャリブレーション
Robust Intrinsic and Extrinsic Calibration of RGB-D Cameras
RGB-Dカメラの内部・外部パラメータを高精度かつ安定に推定する新しい誤差モデルに基づくキャリブレーション手法を提案し、ROS上に実装して実験的に有効性を示した論文。
著者: Filippo Basso, Emanuele Menegatti, Alberto Pretto
分類: cs.CV, cs.RO
原文アブストラクト
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.
