针对相对封闭、磁干扰等特殊环境下传感器应用受限,导致爬壁机器人自主定位误差随时间累积的问题,该文提出并实现了一种基于外部RGB-D相机和惯性测量单元(inertial measurement unit,IMU)组合的爬壁机器人自主定位方法。该方法采用深度学习和核相关滤波(kernelized correlation filter,KCF)组合的目标跟踪方法进行初步位置定位；在此基础上,利用法向量方向投影的方法筛选出机器人外壳顶部的中心点,实现机器人定位中的位置定位。推导了机器人底盘法向量、横滚角与航向角的定量关系,设计了串联的扩展Kalman滤波器(extended Kalman filter,EKF)计算横滚角、俯仰角和航向角,实现机器人定位中的姿态估计。实验结果表明：该方法使爬壁机器人位置定位误差小于0.02 m,姿态估计的航向角和横滚角误差小于2.5°,俯仰角误差小于1.5°,有效地提高了爬壁机器人定位精度。
Abstract
Sensor accuracy in special environments can be very limited due to closed systems and magnetic interference. For example, sensors on wall climbing robots can experience accumulation of autonomous positioning errors with time. The paper presents an autonomous positioning method for wall climbing robots based on an external RGB-D camera and a robot-mounted inertial measurement unit (IMU). This method uses the target tracking method with a deep learning and kernelized correlation filter (KCF) for preliminary positioning. A normal direction projection method is then used to locate the center on the top of the robot shell for the robot position positioning. The system determines the normal, the roll angle and the heading of the robot with a series EKF filter calculating the roll angle, pitch angle and heading to estimate the robot attitude. Tests show that the wall climbing robot positioning error is within 0.02 m, the heading error and the roll angle error for the attitude estimate are both within 2.5°, and the pitch angle error is within 1.5°. This system effectively improves the wall climbing robot positioning accuracy.
关键词
RGB-D相机 /
三维点云 /
扩展Kalman滤波器 /
爬壁机器人 /
惯性测量单元
Key words
RGB-D cameras /
3D point cloud /
extended Kalman filters /
wall climbing robots /
inertial measurement unit
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基金
孙振国,副教授,E-mail:sunzhg@tsinghua.edu.cn
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