LASR_CV: VISION-BASED RELATIVE NAVIGATION AND PROXIMITY OPERATIONS PIPELINE
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To solve the Simultaneous Localization and Mapping (SLAM) problem is to calculate one's own six degree-of-freedom motion with respect to an unknown scene, and to simultaneously generate a three-dimensional map of the scene. This paper presents LASR-CV, a computational vision pipeline for solving the SLAM problem in real time, created by the Land, Air, and Space Robotics (LASR) Lab at Texas A&M University. A modular and extensible framework, LASR-CV is designed for rapid-prototyping of algorithms and sensors for estimation and computer vision. LASR-CV consists of several modules operating in parallel to generate frame-rate pose estimates and geometric models. This modular architecture decouples research topics of interest from the SLAM problem as a whole, enabling developers and researchers to test their software or hardware easily. Each module has "hooks" into the internal data to enable algorithmic tuning or report generation. When combined with inertial measurements, detailed error studies of individual sensors or algorithms can be performed. In this paper, LASR-CV is applied to a laboratory-scale version of an asteroid approach and survey mission. Relative measurements are provided by a Microsoft Kinect active stereo sensor, and the SLAM problem is solved for a general rotating and translating motion, the end result being a high-fidelity three-dimensional reconstruction of a mock asteroid and the relative position and orientation of the mock spacecraft.
