A Novel Position and Orientation Sensor for Indoor Navigation Based on Linear CCDs The position and orientation of a mobile agent, such as robot or drone, etc., should be estimated in a timely way during operation in the structured indoor environment, so as to ensure the security and efficiency of task execution. Concerning the problem that the position and orientation are often estimated separately by different kinds of sensors in the offtheshelf methods, we design a novel position orientation sensor (POS). The POS consists of four pairs of linear chargecoupled devices (CCDs) and cylindrical lenses, which can estimate the 3D coordinate of the anchor in the POSxe2x80x99s field of view. After detecting at least three anchors in its field of vision sequentially, the Rodrigues coordinate transformation algorithm is utilized to estimate the position and orientation of POS simultaneously. Meanwhile, the position and orientation are estimated at the receiver side. Hence there is no privacy concern associated with this system. The architecture of the proposed POS is symmetrical and redundant, even if one of the linear CCDs or cylindrical lens malfunctions, the whole system could still work normally. The proposed method is costeffective and easily extends to a wide range. The numerical simulation demonstrates the feasibility and high accuracy of the proposed method, and it outperforms the offtheshelf methods.

Active Infrared Coded Target Design and Pose Estimation for Multiple Objects Relative pose estimation is critical for collaborative multiagent systems. To achieve accurate and lowcost localization in cluttered and GPSdenied environments, we propose a novel relative pose estimation system based on a designed active infrared coded target. Specifically, each agent is equipped with a forwardlooking monocular camera and a unique infrared coded target. The target with the unique lighted LED arrangement is detected by the camera and processed with an efficient decoding algorithm. The relative pose between the agent and the camera is estimated by combining a PnP algorithm and a Kalman filter. Various experiments are performed to show that the proposed pose estimation system is accurate, robust and efficient in cluttered and GPSdenied environments.