Real-time scalable 6DOF pose estimation for textureless objects
Real-time scalable 6DOF pose estimation for textureless objects
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DOI：
10.1109/icra.2016.7487396
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发表时间：
2016-05
期刊：
影响因子：
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通讯作者：
Zhe Cao;Yaser Sheikh;N. Banerjee
中科院分区：
文献类型：
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作者：
Zhe Cao;Yaser Sheikh;N. Banerjee
无纹理物体的6DOF姿态的实时识别是机器人技术中的一个基本和具有挑战性的问题。我们提出了一种新的方法来实时估计RGB和RGB- d图像捕获中物体的视点、尺度和平移。在这项工作中，我们使用3D模型来渲染无纹理对象的示例姿势，并使用GPU实现找到与输入图像最接近的匹配。为了在物体上实现光照和外观的不变性，我们将图像转换为高斯空间的拉普拉斯变换。为了实现实时匹配，我们引入了一种新的模板集和图像的重塑，并重构了传统的归一化相互关联操作，以利用GPU进行快速矩阵-矩阵乘法。通过主成分分析的降维方法和候选消除方法，进一步提高了大规模模板匹配的速度。我们的方法通过定性结果和与已有方法的定量比较，达到了最先进的性能。
Real-time recognition of the 6DOF pose of textureless objects is a fundamental and challenging problem in robotics. We present a novel approach to perform real-time estimation of the viewpoint, scale, and translation of an object in RGB and RGB-D image captures. In this work, we use a 3D model to render example poses of a textureless object, and find the nearest match to the input image using a GPU implementation. To achieve invariance to illumination and appearance across an object, we transform images to the Laplacian of Gaussian space. To perform real-time matching, we introduce a novel reshaping of the template set and the image, and we restructure the traditional normalized cross-correlation operation to leverage the GPU for fast matrix-matrix multiplication. We provide further speed up of large-scale template matching by contributing a dimensionality reduction approach using principal component analysis, and a candidate elimination method. Our method achieves state-of-the-art performance as shown by qualitative results and quantitative comparisons to pre-existing methods.