Ye Cang, Hong Soonhac, Tamjidi Amirhossein
Department of Systems Engineering, University of Arkansas at Little Rock, Little Rock, AR 72204, USA.
IEEE Trans Autom Sci Eng. 2015 Oct;12(4):1169-1180. doi: 10.1109/TASE.2015.2469726. Epub 2015 Oct 5.
This paper presents a 6-DOF Pose Estimation (PE) method for a Robotic Navigation Aid (RNA) for the visually impaired. The RNA uses a single 3D camera for PE and object detection. The proposed method processes the camera's intensity and range data to estimates the camera's egomotion that is then used by an Extended Kalman Filter (EKF) as the motion model to track a set of visual features for PE. A RANSAC process is employed in the EKF to identify inliers from the visual feature correspondences between two image frames. Only the inliers are used to update the EKF's state. The EKF integrates the egomotion into the camera's pose in the world coordinate system. To retain the EKF's consistency, the distance between the camera and the floor plane (extracted from the range data) is used by the EKF as the observation of the camera's coordinate. Experimental results demonstrate that the proposed method results in accurate pose estimates for positioning the RNA in indoor environments. Based on the PE method, a wayfinding system is developed for localization of the RNA in a home environment. The system uses the estimated pose and the floorplan to locate the RNA user in the home environment and announces the points of interest and navigational commands to the user through a speech interface.
This work was motivated by the limitations of the existing navigation technology for the visually impaired. Most of the existing methods use a point/line measurement sensor for indoor object detection. Therefore, they lack capability in detecting 3D objects and positioning a blind traveler. Stereovision has been used in recent research. However, it cannot provide reliable depth data for object detection. Also, it tends to produce a lower localization accuracy because its depth measurement error quadratically increases with the true distance. This paper suggests a new approach for navigating a blind traveler. The method uses a single 3D time-of-flight camera for both 6-DOF PE and 3D object detection and thus results in a small-sized but powerful RNA. Due to the camera's constant depth accuracy, the proposed egomotion estimation method results in a smaller error than that of existing methods. A new EKF method is proposed to integrate the egomotion into the RNA's 6-DOF pose in the world coordinate system by tracking both visual and geometric features of the operating environment. The proposed method substantially reduces the pose error of a standard EKF method and thus supports a longer range navigation task. One limitation of the method is that it requires a feature-rich environment to work well.
本文提出了一种用于视障人士的机器人导航辅助设备（RNA）的六自由度姿态估计（PE）方法。该RNA使用单个3D相机进行姿态估计和目标检测。所提出的方法处理相机的强度和距离数据，以估计相机的自我运动，然后扩展卡尔曼滤波器（EKF）将其用作运动模型来跟踪一组视觉特征以进行姿态估计。在EKF中采用随机抽样一致性（RANSAC）过程，从两个图像帧之间的视觉特征对应关系中识别内点。仅使用内点来更新EKF的状态。EKF将自我运动整合到相机在世界坐标系中的姿态中。为了保持EKF的一致性，EKF将相机与地面平面之间的距离（从距离数据中提取）用作相机z坐标的观测值。实验结果表明，所提出的方法能够准确估计姿态，以便在室内环境中定位RNA。基于姿态估计方法，开发了一种用于在家庭环境中定位RNA的路径查找系统。该系统使用估计的姿态和平面图在家庭环境中定位RNA用户，并通过语音接口向用户宣布兴趣点和导航命令。
这项工作是受现有视障人士导航技术的局限性所推动。大多数现有方法使用点/线测量传感器进行室内目标检测。因此，它们缺乏检测三维物体和定位盲人旅行者的能力。立体视觉已在近期研究中使用。然而，它不能为目标检测提供可靠的深度数据。而且，它往往会产生较低的定位精度，因为其深度测量误差会随着真实距离呈二次方增加。本文提出了一种引导盲人旅行者的新方法。该方法使用单个3D飞行时间相机进行六自由度姿态估计和三维目标检测，从而得到一个体积小但功能强大的RNA。由于相机具有恒定的深度精度，所提出的自我运动估计方法产生的误差比现有方法更小。提出了一种新的EKF方法，通过跟踪操作环境的视觉和几何特征，将自我运动整合到RNA在世界坐标系中的六自由度姿态中。所提出的方法大幅降低了标准EKF方法的姿态误差，从而支持更长距离的导航任务。该方法的一个局限性是它需要一个特征丰富的环境才能良好运行。
