为有效解决单一传感器同时定位与地图构建(simultaneous localization and mapping, SLAM)定位精度低、障碍物识别不全问题,提出一种多传感器融合的SLAM方法。通过将RGB-D相机采集的点云进行降采样、滤波处理,极大降低算法的计算量。利...为有效解决单一传感器同时定位与地图构建(simultaneous localization and mapping, SLAM)定位精度低、障碍物识别不全问题,提出一种多传感器融合的SLAM方法。通过将RGB-D相机采集的点云进行降采样、滤波处理,极大降低算法的计算量。利用点云库对激光点云和降采样RGB-D相机点云进行融合,融合的点云利用PL-ICP完成点云配准,提高对外部环境的准确识别。利用扩展卡尔曼滤波融合IMU和轮式里程计与点云进行位姿匹配,保证定位的精度。实验结果表明,该方法可以有效提高对室内建图和导航的精度。展开更多
针对传统激光SLAM算法在转角、走廊等退化环境下,出现系统精度低、算法失效以及传感器运行累计误差等问题,提出一种多传感器融合的SLAM算法。首先,构建机器人运动模型,激光雷达、IMU、轮式里程计传感器测量模型,并分离出各传感器残差项...针对传统激光SLAM算法在转角、走廊等退化环境下,出现系统精度低、算法失效以及传感器运行累计误差等问题,提出一种多传感器融合的SLAM算法。首先,构建机器人运动模型,激光雷达、IMU、轮式里程计传感器测量模型,并分离出各传感器残差项,以便后端算法修正IMU零偏和轮式里程计测量噪声;其次,通过拓展卡尔曼滤波算法融合轮式里程计IMU数据,补偿机器人里程计精确度;最后,将整体最大后验概率问题(Maximum A Posteriori Estimation,MAP)转换为最小二乘问题,利用后端QR算法提高求解矩阵准确度。长走廊环境中,所提方法地图还原精度较LIO-SAM提高了32.61%;在相同实验场景中,机器人定位精度较LIO-SAM提高了35.47%。实验结果表明,所提方法具有较高的地图还原度与定位精度。展开更多
Sensor scheduling is essential to collaborative target tracking in wireless sensor networks （WSNs）. In the existing works for target tracking in WSNs, such as the information-driven sensor query （IDSQ）, the taskin...Sensor scheduling is essential to collaborative target tracking in wireless sensor networks （WSNs）. In the existing works for target tracking in WSNs, such as the information-driven sensor query （IDSQ）, the tasking sensors are scheduled to maximize the information gain while minimizing the resource cost based on the uniform sampling intervals, ignoring the changing of the target dynamics and the specific desirable tracking goals. This paper proposes a novel energy-efficient adaptive sensor scheduling approach that jointly selects tasking sensors and determines their associated sampling intervals according to the predicted tracking accuracy and tracking energy cost. At each time step, the sensors are scheduled in alternative tracking mode, namely, the fast tracking mode with smallest sampling interval or the tracking maintenance mode with larger sampling interval, according to a specified tracking error threshold. The approach employs an extended Kalman filter （EKF）-based estimation technique to predict the tracking accuracy and adopts an energy consumption model to predict the energy cost. Simulation results demonstrate that, compared to a non-adaptive sensor scheduling approach, the proposed approach can save energy cost significantly without degrading the tracking accuracy.展开更多
The extended gate field effect transistor (EGFET)has many advantages such as the fabrication is easy,low cost, easy to operate etc.The EGFET was applied to biosensor in recent years.In this study,the tin oxide (SnO_2)...The extended gate field effect transistor (EGFET)has many advantages such as the fabrication is easy,low cost, easy to operate etc.The EGFET was applied to biosensor in recent years.In this study,the tin oxide (SnO_2)pH sensitive membrane was deposited on ITO glass,when the surface voltage which pH membrane changes,the gate voltage and current channel of MOSFET will change immediately to detect concentration of the glucose sensor.In this study we have devoted to research about the calibration of the circuit measurement for the glucose sensor,and study the calibration system of the drift and hysteresis.展开更多
文摘为有效解决单一传感器同时定位与地图构建(simultaneous localization and mapping, SLAM)定位精度低、障碍物识别不全问题,提出一种多传感器融合的SLAM方法。通过将RGB-D相机采集的点云进行降采样、滤波处理,极大降低算法的计算量。利用点云库对激光点云和降采样RGB-D相机点云进行融合,融合的点云利用PL-ICP完成点云配准,提高对外部环境的准确识别。利用扩展卡尔曼滤波融合IMU和轮式里程计与点云进行位姿匹配,保证定位的精度。实验结果表明,该方法可以有效提高对室内建图和导航的精度。
文摘针对传统激光SLAM算法在转角、走廊等退化环境下,出现系统精度低、算法失效以及传感器运行累计误差等问题,提出一种多传感器融合的SLAM算法。首先,构建机器人运动模型,激光雷达、IMU、轮式里程计传感器测量模型,并分离出各传感器残差项,以便后端算法修正IMU零偏和轮式里程计测量噪声;其次,通过拓展卡尔曼滤波算法融合轮式里程计IMU数据,补偿机器人里程计精确度;最后,将整体最大后验概率问题(Maximum A Posteriori Estimation,MAP)转换为最小二乘问题,利用后端QR算法提高求解矩阵准确度。长走廊环境中,所提方法地图还原精度较LIO-SAM提高了32.61%;在相同实验场景中,机器人定位精度较LIO-SAM提高了35.47%。实验结果表明,所提方法具有较高的地图还原度与定位精度。
基金partly supported by the Agency for Science,Technology and Research(A*Star)SERC(No.0521010037,0521210082)
文摘Sensor scheduling is essential to collaborative target tracking in wireless sensor networks （WSNs）. In the existing works for target tracking in WSNs, such as the information-driven sensor query （IDSQ）, the tasking sensors are scheduled to maximize the information gain while minimizing the resource cost based on the uniform sampling intervals, ignoring the changing of the target dynamics and the specific desirable tracking goals. This paper proposes a novel energy-efficient adaptive sensor scheduling approach that jointly selects tasking sensors and determines their associated sampling intervals according to the predicted tracking accuracy and tracking energy cost. At each time step, the sensors are scheduled in alternative tracking mode, namely, the fast tracking mode with smallest sampling interval or the tracking maintenance mode with larger sampling interval, according to a specified tracking error threshold. The approach employs an extended Kalman filter （EKF）-based estimation technique to predict the tracking accuracy and adopts an energy consumption model to predict the energy cost. Simulation results demonstrate that, compared to a non-adaptive sensor scheduling approach, the proposed approach can save energy cost significantly without degrading the tracking accuracy.
文摘The extended gate field effect transistor (EGFET)has many advantages such as the fabrication is easy,low cost, easy to operate etc.The EGFET was applied to biosensor in recent years.In this study,the tin oxide (SnO_2)pH sensitive membrane was deposited on ITO glass,when the surface voltage which pH membrane changes,the gate voltage and current channel of MOSFET will change immediately to detect concentration of the glucose sensor.In this study we have devoted to research about the calibration of the circuit measurement for the glucose sensor,and study the calibration system of the drift and hysteresis.
