A Kinect-based real-time compressive tracking prototype system for amphibious spherical robots.
A Kinect-based real-time compressive tracking prototype system for amphibious spherical robots.
复制标题
基于kinect的两栖球形机器人实时压缩跟踪原型系统
DOI：
10.3390/s150408232
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发表时间：
2015-04-08
期刊：
影响因子：
--
通讯作者：
Guo S
中科院分区：
文献类型：
--
作者：
Pan S;Shi L;Guo S
视觉跟踪系统是视觉伺服、自主导航、路径规划、人机交互和其他机器人功能的基础。移动的机器人要在多样化和不断变化的环境中执行各种任务，对视觉跟踪系统的鲁棒性、精度、环境适应性和实时性提出了很高的要求。针对2012年提出的用于灵活、经济的水下探测的两栖球形机器人的应用特点，提出并实现了一种改进的RGB-D视觉跟踪算法。考虑到移动的机器人有限的电源和计算能力，压缩跟踪（CT），这是在2012年提出的有效和高效的算法，被选为所提出的算法的基础上处理彩色图像。采用二阶运动模型的卡尔曼滤波器预测目标的状态，并为CT跟踪器选择候选块或样本。此外，使用具有卡尔曼估计机制的方差比特征偏移（VR-V）跟踪器来处理深度图像。使用反馈策略，深度跟踪结果被用来帮助CT跟踪器以自适应速率更新分类器参数。该方法部分解决了CT的漂移、对遮挡和目标高速运动鲁棒性差等问题。为了评估所提出的算法，微软Kinect传感器，它结合了彩色和红外深度摄像头，被采用的机器人跟踪系统的原型中使用。不同图像序列的实验结果表明了该跟踪系统的有效性、鲁棒性和实时性。
A visual tracking system is essential as a basis for visual servoing, autonomous navigation, path planning, robot-human interaction and other robotic functions. To execute various tasks in diverse and ever-changing environments, a mobile robot requires high levels of robustness, precision, environmental adaptability and real-time performance of the visual tracking system. In keeping with the application characteristics of our amphibious spherical robot, which was proposed for flexible and economical underwater exploration in 2012, an improved RGB-D visual tracking algorithm is proposed and implemented. Given the limited power source and computational capabilities of mobile robots, compressive tracking (CT), which is the effective and efficient algorithm that was proposed in 2012, was selected as the basis of the proposed algorithm to process colour images. A Kalman filter with a second-order motion model was implemented to predict the state of the target and select candidate patches or samples for the CT tracker. In addition, a variance ratio features shift (VR-V) tracker with a Kalman estimation mechanism was used to process depth images. Using a feedback strategy, the depth tracking results were used to assist the CT tracker in updating classifier parameters at an adaptive rate. In this way, most of the deficiencies of CT, including drift and poor robustness to occlusion and high-speed target motion, were partly solved. To evaluate the proposed algorithm, a Microsoft Kinect sensor, which combines colour and infrared depth cameras, was adopted for use in a prototype of the robotic tracking system. The experimental results with various image sequences demonstrated the effectiveness, robustness and real-time performance of the tracking system.