Googleストリートビューを用いたメートル精度の自己位置推定
Metric Localization using Google Street View
単眼カメラ映像とオドメトリのみを入力とし、Googleストリートビューのパノラマ画像を全球測位の基準として非線形最小二乗法で自己位置を推定する手法を提案した。
著者: Pratik Agarwal, Wolfram Burgard, Luciano Spinello
分類: cs.RO, cs.CV
原文アブストラクト
Accurate metrical localization is one of the central challenges in mobile robotics. Many existing methods aim at localizing after building a map with the robot. In this paper, we present a novel approach that instead uses geotagged panoramas from the Google Street View as a source of global positioning. We model the problem of localization as a non-linear least squares estimation in two phases. The first estimates the 3D position of tracked feature points from short monocular camera sequences. The second computes the rigid body transformation between the Street View panoramas and the estimated points. The only input of this approach is a stream of monocular camera images and odometry estimates. We quantified the accuracy of the method by running the approach on a robotic platform in a parking lot by using visual fiducials as ground truth. Additionally, we applied the approach in the context of personal localization in a real urban scenario by using data from a Google Tango tablet.
