6-DOF pose estimation of a Portable Navigation Aid for the visually impaired

Abstract

In this paper, we present a 6-DOF pose estimation method for a Portable Navigation Aid for the visually impaired. The navigation aid uses a single 3D camera-SwissRanger SR4000-for both pose estimation and object/obstacle detection. The SR4000 provides intensity and range data of the scene. These data are simultaneously processed to estimate the camera's egomotion, which is then used as the motion model by an Extended Kalman Filter (EKF) to track the visual features maintained in a local map. In order to create correct feature correspondences between images, a 3-point RANSAC (RANdom SAmple Consensus) process is devised to identify the inliers from the feature correspondences based on the SIFT (Scale Invariant Feature Transform) descriptors. Only the inliers are used to update the EKF's state. Additional inliers caused by the updated state are then located and used to perform another state update. The EKF integrates the egomotion into the camera's pose in the world coordinate with a relatively small error. Since the camera's y coordinate may be measured as the distance between the camera and the floor plane, it is used as an additional observation in this work. Experimental results indicate that the proposed pose estimation method results in accurate pose estimates for positioning the visually impaired in an indoor environment.