There are about 253 million people with visual impairment worldwide.Many of them use a white cane and/or a guide dog as the mobility tool for daily travel.Despite decades of efforts,electronic navigation aid that can ...There are about 253 million people with visual impairment worldwide.Many of them use a white cane and/or a guide dog as the mobility tool for daily travel.Despite decades of efforts,electronic navigation aid that can replace white cane is still research in progress.In this paper,we propose an RGB-D camera based visual positioning system(VPS)for real-time localization of a robotic navigation aid(RNA)in an architectural floor plan for assistive navigation.The core of the system is the combination of a new 6-DOF depth-enhanced visual-inertial odometry(DVIO)method and a particle filter localization(PFL)method.DVIO estimates RNA’s pose by using the data from an RGB-D camera and an inertial measurement unit(IMU).It extracts the floor plane from the camera’s depth data and tightly couples the floor plane,the visual features(with and without depth data),and the IMU’s inertial data in a graph optimization framework to estimate the device’s 6-DOF pose.Due to the use of the floor plane and depth data from the RGB-D camera,DVIO has a better pose estimation accuracy than the conventional VIO method.To reduce the accumulated pose error of DVIO for navigation in a large indoor space,we developed the PFL method to locate RNA in the floor plan.PFL leverages geometric information of the architectural CAD drawing of an indoor space to further reduce the error of the DVIO-estimated pose.Based on VPS,an assistive navigation system is developed for the RNA prototype to assist a visually impaired person in navigating a large indoor space.Experimental results demonstrate that:1)DVIO method achieves better pose estimation accuracy than the state-of-the-art VIO method and performs real-time pose estimation(18 Hz pose update rate)on a UP Board computer;2)PFL reduces the DVIO-accrued pose error by 82.5%on average and allows for accurate wayfinding(endpoint position error≤45 cm)in large indoor spaces.展开更多
针对助行机器人的柔顺性和安全性问题,基于多传感器系统融合技术,本文提出了一种能够兼具柔顺与安全的助行机器人运动控制方法.首先介绍了助行机器人的机械结构、控制原理以及多传感器系统,然后根据机器人多传感器系统,设计出各传感器...针对助行机器人的柔顺性和安全性问题,基于多传感器系统融合技术,本文提出了一种能够兼具柔顺与安全的助行机器人运动控制方法.首先介绍了助行机器人的机械结构、控制原理以及多传感器系统,然后根据机器人多传感器系统,设计出各传感器相对应的用户意图估计方法,提出了一种基于多传感器融合的助行机器人柔顺运动控制算法.分析用户可能发生的跌倒模式,使用基于卡尔曼滤波(Kalman filter,KF)的序贯概率比检验(Sequential probability ratio test,SPRT)方法和决策函数来判断用户是否会跌倒,并判断处于哪种跌倒模式.最后,通过助行机器人柔顺运动控制实验和用户跌倒检测实验验证了算法的有效性.展开更多
基金supported by the NIBIB and the NEI of the National Institutes of Health(R01EB018117)。
文摘There are about 253 million people with visual impairment worldwide.Many of them use a white cane and/or a guide dog as the mobility tool for daily travel.Despite decades of efforts,electronic navigation aid that can replace white cane is still research in progress.In this paper,we propose an RGB-D camera based visual positioning system(VPS)for real-time localization of a robotic navigation aid(RNA)in an architectural floor plan for assistive navigation.The core of the system is the combination of a new 6-DOF depth-enhanced visual-inertial odometry(DVIO)method and a particle filter localization(PFL)method.DVIO estimates RNA’s pose by using the data from an RGB-D camera and an inertial measurement unit(IMU).It extracts the floor plane from the camera’s depth data and tightly couples the floor plane,the visual features(with and without depth data),and the IMU’s inertial data in a graph optimization framework to estimate the device’s 6-DOF pose.Due to the use of the floor plane and depth data from the RGB-D camera,DVIO has a better pose estimation accuracy than the conventional VIO method.To reduce the accumulated pose error of DVIO for navigation in a large indoor space,we developed the PFL method to locate RNA in the floor plan.PFL leverages geometric information of the architectural CAD drawing of an indoor space to further reduce the error of the DVIO-estimated pose.Based on VPS,an assistive navigation system is developed for the RNA prototype to assist a visually impaired person in navigating a large indoor space.Experimental results demonstrate that:1)DVIO method achieves better pose estimation accuracy than the state-of-the-art VIO method and performs real-time pose estimation(18 Hz pose update rate)on a UP Board computer;2)PFL reduces the DVIO-accrued pose error by 82.5%on average and allows for accurate wayfinding(endpoint position error≤45 cm)in large indoor spaces.
文摘针对助行机器人的柔顺性和安全性问题,基于多传感器系统融合技术,本文提出了一种能够兼具柔顺与安全的助行机器人运动控制方法.首先介绍了助行机器人的机械结构、控制原理以及多传感器系统,然后根据机器人多传感器系统,设计出各传感器相对应的用户意图估计方法,提出了一种基于多传感器融合的助行机器人柔顺运动控制算法.分析用户可能发生的跌倒模式,使用基于卡尔曼滤波(Kalman filter,KF)的序贯概率比检验(Sequential probability ratio test,SPRT)方法和决策函数来判断用户是否会跌倒,并判断处于哪种跌倒模式.最后,通过助行机器人柔顺运动控制实验和用户跌倒检测实验验证了算法的有效性.
