杨东方, 王仕成, 刘华平, 刘志国, 孙富春. 基于Kinect系统的场景建模与机器人自主导航[J]. 机器人, 2012, 34(5): 581-589. DOI: 10.3724/SP.J.1218.2012.00581
引用本文: 杨东方, 王仕成, 刘华平, 刘志国, 孙富春. 基于Kinect系统的场景建模与机器人自主导航[J]. 机器人, 2012, 34(5): 581-589. DOI: 10.3724/SP.J.1218.2012.00581
YANG Dongfang, WANG Shicheng, LIU Huaping, LIU Zhiguo, SUN Fuchun. Scene Modeling and Autonomous Navigation for Robots Based on Kinect System[J]. ROBOT, 2012, 34(5): 581-589. DOI: 10.3724/SP.J.1218.2012.00581
Citation: YANG Dongfang, WANG Shicheng, LIU Huaping, LIU Zhiguo, SUN Fuchun. Scene Modeling and Autonomous Navigation for Robots Based on Kinect System[J]. ROBOT, 2012, 34(5): 581-589. DOI: 10.3724/SP.J.1218.2012.00581

基于Kinect系统的场景建模与机器人自主导航

Scene Modeling and Autonomous Navigation for Robots Based on Kinect System

  • 摘要: 本文分别基于微软Kinect系统的单目RGB摄像机以及深度距离受限的RGB-D像机, 研究解决室内机器人的6自由度定位问题. 首先,在传统不完全自由度估计的基础上, 提出了特征点参数的增量式模型以解决运动尺度不确定性问题. 该模型和以往的欧几里得、逆深度参数化模型相比,不仅能够显著降低系统状态维数, 而且能够保证系统状态的一致可观测性; 此外,基于增量式模型, 根据Kinect系统中采集的RGB图像和红外图像,实现了对机器人6自由度的运动估计. 最后, 将Kinect系统采集得到的RGB图像和深度图像序列用于欧几里得参数化模型和增量式参数化模型, 对应的实验结果证明了本文所提的自主导航方法的有效性.

     

    Abstract: Monocular RGB camera and distance-limited RGB-D camera with Microsoft Kinect system are respectively utilized to solve the 6DoF navigation problem for indoor robots. At first, based on the traditional partial-DoF pose estimation algorithms, an incremental parameterized model is proposed for feature-points' parameters, which is able to solve the scale ambiguity problem. This model differs from Euclid and inverse depth parameterized models with the sharply reduced state-dimension and the consistent observability of system state. In addition, based on this proposed incremental parameterized model, the 6DoF motion is estimated by the observed image-sequence that comes from the RGB camera or infrared camera. At the end of this paper, both the RGB and depth image sequences that sampled from the Kinect system are utilized in Euclid parameterized model and the incremental parameterized model. And the corresponding results validate the effectiveness of the proposed autonomous navigation approach.

     

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