マルチビュー最適化によるRGB画像からのテクスチャレス物体の6D姿勢推定
6D Pose Estimation for Textureless Objects on RGB Frames using Multi-View Optimization
複数視点のRGB画像のみを用い、6D姿勢推定を並進と回転の2段階に分けて解くことで、テクスチャのない物体の姿勢を高精度に推定するフレームワークを提案した。
著者: Jun Yang, Wenjie Xue, Sahar Ghavidel, Steven L. Waslander
分類: cs.RO
原文アブストラクト
6D pose estimation of textureless objects is a valuable but challenging task for many robotic applications. In this work, we propose a framework to address this challenge using only RGB images acquired from multiple viewpoints. The core idea of our approach is to decouple 6D pose estimation into a sequential two-step process, first estimating the 3D translation and then the 3D rotation of each object. This decoupled formulation first resolves the scale and depth ambiguities in single RGB images, and uses these estimates to accurately identify the object orientation in the second stage, which is greatly simplified with an accurate scale estimate. Moreover, to accommodate the multi-modal distribution present in rotation space, we develop an optimization scheme that explicitly handles object symmetries and counteracts measurement uncertainties. In comparison to the state-of-the-art multi-view approach, we demonstrate that the proposed approach achieves substantial improvements on a challenging 6D pose estimation dataset for textureless objects.
