Long-range outdoor monocular localization with active features for ship air wake measurement

Abstract

Monocular pose estimation is a well-studied aspect of computer vision with a wide array of applications, including camera calibration, autonomous navigation, object pose tracking, augmented reality, and numerous other areas. However, some unexplored areas of camera pose estimation remain academically interesting. This thesis provides a detailed description of the system hardware and software that permits operation in one application area in particular: long-range, precise monocular pose estimation in feature-starved environments. The novel approach to pose extraction uses special hardware, including active LED features and a bandpass-interference optical filter, to significantly simplify the image processing step of the Perspective-n-Point (PnP) problem. The PnP problem describes the calculation of pose from n extracted image points corresponding to n known 3D world points. The proposed application method operates in tandem with a tethered unmanned aerial vehicle (UAV) and mobile ground control station (GCS). The integrated localization and flight system serves as a platform for future U.S. Navy air flow research. Indoor tests at the RAVEN flight space of MIT's Aerospace Controls Lab and outdoor tests at a grass strip runway demonstrate the system's efficacy in providing an accurate and precise pose estimate of the UAV relative to the mobile GCS.