A reliable and accurate visual object 6DoF pose tracking system for texture-less objects plays an important role in various fields of modern industry hence it has been a hot research topic for decades. Traditional feature-based pose tracking methods require rich feature points on objects, and it cannot handle texture-less objects. To tackle this problem, the paper proposes a novel edge-based method for continuous 6DOF pose tracking of texture-less objects. The pose of the object is estimated by minimizing the matching error between extracted image edges and re-projected CAD model edges. The matching error is represented using the Directional Chamfer Matching (DCM) Tensor. Compared with previous methods, the proposed system improves the overall performance of pose tracking system in two ways. Firstly, the method proposes an analytical mathematic model in the optimization process; Secondly, the method proposes an adaptive edge point weighting algorithm to tackle the occlusion or edge weakness problem. Both methods help improve the accuracy and robustness of the pose estimation system. With the benefit of GPU acceleration on DCM Tensor calculation the proposed method could run in real time. Extensive experiments are conducted both on public datasets and CG-rendered synthetic datasets to validate the accuracy and robustness of the proposed algorithm against other state-of-the-art methods.

中文翻译：

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针对无纹理物体的准确 6DOF 姿势跟踪


针对无纹理物体的可靠且准确的视觉物体6DoF位姿跟踪系统在现代工业的各个领域中发挥着重要作用，因此几十年来一直是研究热点。传统的基于特征的姿态跟踪方法需要物体上丰富的特征点，并且无法处理无纹理的物体。为了解决这个问题，本文提出了一种基于边缘的新方法，用于无纹理物体的连续 6DOF 位姿跟踪。通过最小化提取的图像边缘和重新投影的 CAD 模型边缘之间的匹配误差来估计物体的姿态。匹配误差使用方向倒角匹配 (DCM) 张量表示。与以前的方法相比，所提出的系统从两个方面提高了姿态跟踪系统的整体性能。首先，该方法提出了优化过程中的解析数学模型；其次，该方法提出了一种自适应边缘点加权算法来解决遮挡或边缘薄弱问题。两种方法都有助于提高位姿估计系统的准确性和鲁棒性。借助 GPU 对 DCM 张量计算的加速，所提出的方法可以实时运行。在公共数据集和 CG 渲染的合成数据集上进行了大量的实验，以验证所提出的算法相对于其他最先进方法的准确性和鲁棒性。