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首页> 外文期刊>Journal of Intelligent & Robotic Systems: Theory & Application >Robotic Hand Pose Estimation Based on Stereo Vision and GPU-enabled Internal Graphical Simulation
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Robotic Hand Pose Estimation Based on Stereo Vision and GPU-enabled Internal Graphical Simulation

机译：基于立体视觉和基于GPU的内部图形仿真的机器人手姿估计

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摘要

Humanoid robots have complex kinematic chains whose modeling is error prone. If the robot model is not well calibrated, its hand pose cannot be determined precisely from the encoder readings, and this affects reaching and grasping accuracy. In our work, we propose a novel method to simultaneously i) estimate the pose of the robot hand, and ii) calibrate the robot kinematic model. This is achieved by combining stereo vision, proprioception, and a 3D computer graphics model of the robot. Notably, the use of GPU programming allows to perform the estimation and calibration in real time during the execution of arm reaching movements. Proprioceptive information is exploited to generate hypotheses about the visual appearance of the hand in the camera images, using the 3D computer graphics model of the robot that includes both kinematic and texture information. These hypotheses are compared with the actual visual input using particle filtering, to obtain both i) the best estimate of the hand pose and ii) a set of joint offsets to calibrate the kinematics of the robot model. We evaluate two different approaches to estimate the 6D pose of the hand from vision (silhouette segmentation and edges extraction) and show experimentally that the pose estimation error is considerably reduced with respect to the nominal robot model. Moreover, the GPU implementation ensures a performance about 3 times faster than the CPU one, allowing real-time operation.
机译：类人机器人具有复杂的运动链，其运动模型易于出错。如果机器人模型未正确校准，则无法从编码器读数中精确确定其手部姿势，这会影响到达和抓握的准确性。在我们的工作中，我们提出了一种新颖的方法来同时i）估计机器人手的姿势，ii）校准机器人运动学模型。这是通过结合机器人的立体视觉，本体感觉和3D计算机图形模型来实现的。值得注意的是，GPU编程的使用允许在执行手臂到达运动的过程中实时执行估计和校准。利用包括运动学信息和纹理信息的机器人的3D计算机图形模型，可以利用本体感受信息来生成有关手在相机图像中的视觉外观的假设。将这些假设与使用粒子滤波的实际视觉输入进行比较，以获取i）手势的最佳估计值和ii）一组关节偏移量以校准机器人模型的运动学。我们评估了两种不同的方法来从视觉上估计手的6D姿势（轮廓分割和边缘提取），并通过实验表明，相对于标称机器人模型，姿势估计误差已大大降低。此外，GPU的实现可确保性能比CPU快3倍，从而实现实时操作。

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