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

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.