6-DOF Pose Estimation of a Robotic Navigation Aid by Tracking Visual and Geometric Features

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 z 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.