Georgia Tech Team Entry for the 2011 AUVSI International Aerial Robotics Competition

Presented at the Third International Aerial Robotics Symposium (IASR), 2011.his paper describes the details of a Quadrotor Unmanned Aerial Vehicle capable of exploring cluttered indoor areas without relying on any external navigational aids. An elaborate Simultaneous Localization and Mapping (SLAM) algorithm is used to fuse information from a laser range sensor, an inertial measurement unit, and an altitude sonar to provide relative position, velocity, and attitude information. A wall-following guidance rule is implemented to ensure that the vehicle explores maximum indoor area in a reasonable amount of time. A model reference adaptive control architecture is used to ensure stability and mitigation of uncertainties. The vehicle is intended to be Georgia Tech Aerial Robotic Team's entry for the 2011 International Aerial Robotics Competition (IARC) Symposium on Indoor Flight Issues

Reconstruction techniques for fixed 3-D lines and fixed 3-D points using the relative pose of one or two cameras

In general, stereovision can be defined as a two part problem. The first is the correspondence problem. This involves determining the image point in each image of a set of images that correspond to the same physical point P. We will call this set of image points, N. The second problem is the reconstruction problem. Once a set of image points, N, that correspond to point P has been determined, N is then used to extract three dimensional information about point P. This master's thesis presents three novel solutions to the reconstruction problem. Two of the techniques presented are for detecting the location of a 3-D point and one for detecting a line expressed in a three dimensional coordinate system. These techniques are tested and validated using a unique 3-D finger detection algorithm. The techniques presented are unique because of their simplicity and because they do not require the cameras to be placed in specific locations, orientations or have specific alignments. On the contrary, it will be shown that the techniques presented in this thesis allow the two cameras used to assume almost any relative pose provided that the object of interest is within their field of view. The relative pose of the cameras at a given instant in time, along with basic equations from the perspective image model are used to form a system of equations that when solved, reveal the 3-D coordinates of a particular fixed point of interest or the three dimensional equation of a fixed line of interest. Finally, it will be shown that a single moving camera can successfully perform the same line and point detection accomplished by two cameras by altering the pose of the camera. The results presented in this work are beneficial to any typical stereovision application because of the computational ease in comparison to other point and line reconstruction techniques. But more importantly, this work allows for a single moving camera to perceive three-dimensional position information, which effectively removes the two camera constraint for a stereo vision system. When used with other monocular cues such as texture or color, the work presented in this thesis could be as accurate as binocular stereo vision at interpreting three dimensional information. Thus, this work could potentially increase the three dimensional perception of a robot that normally uses one camera, such as an eye-in-hand robot or a snake like robot.MSCommittee Chair: Sadegh, Nade

Self-Contained Ranging Sensor Aided Autonomous Guidance, Navigation, and Control for Indoor Flight

Copyright © 2012 by the American Institute of Aeronautics and Astronautics, Inc.DOI: 10.2514/1.55410This paper describes the design and flight test of a completely self-contained autonomous indoor Miniature Unmanned Aerial System (M-UAS). Guidance, navigation, and control algorithms are presented, enabling the M-UAS to autonomously explore cluttered indoor areas without relying on any off-board computation or external navigation aids such as GPS. The system uses a scanning laser rangefinder and a streamlined Simultaneous Localization and Mapping (SLAM) algorithm to provide a position and heading estimate, which is combined with other sensor data to form a six degree-of-freedom inertial navigation solution. This enables an accurate estimate of the vehicle attitude, relative position, and velocity. The state information, with a self-generated map, is used to implement a frontier-based exhaustive search of an indoor environment. Improvements to existing guidance algorithms balance exploration with the need to remain within sensor range of indoor structures such that the SLAM algorithm has sufficient information to form a reliable position estimate. A dilution of precision metric is developed to quantify the effect of environment geometry on the SLAM pose covariance, which is then used to update the 2-D position and heading in the navigation filter. Simulation and flight test results validate the presented algorithms

Integrated Guidance Navigation and Control for a Fully Autonomous Indoor UAS

Copyright © 2011 by authors.Presented at the AIAA Guidance, Navigation, and Control Conference, 8-11 August 2011, Portland, Oregon.DOI: 10.2514/6.2011-6720This paper describes the details of a Quadrotor miniature unmanned aerial system capable of autonomously exploring cluttered indoor areas without relying on any external navigational aids such as GPS. A streamlined Simultaneous Localization and Mapping (SLAM) algorithm is implemented onboard the vehicle to fuse information from a scanning laser range sensor, an inertial measurement unit, and an altitude sonar to provide relative position, velocity, and attitude information. This state information, with a self-generated map, is used to implement a frontier-based exhaustive search of an indoor environment. To ensure the SLAM algorithm has sufficient information to form a reliable solution, the guidance algorithm ensures the vehicle approaches frontier waypoints through a path that remains within sensor range of indoor structures. Along with a detailed description of the system, simulation and hardware testing results are presented