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This paper describes the target detection and tracking architecture used by the Georgia Tech Aerial Robotics team for the American Helicopter Society (AHS) Micro Aerial Vehicle (MAV) challenge. The vision system described enables visionaided navigation with additional abilities such as target detection and tracking all performed onboard the vehicles computer. The author suggests a robust target tracking method that does not solely depend on the image obtained from a camera, but also utilizes the other sensor outputs and runs a target location estimator. The machine learning based target identification method uses Haar-like classifiers to extract the target candidate points. The raw measurements are plugged into multiple Extended Kalman Filters (EKFs). The statistical test (Z-test) is used to bound the measurement, and solve the corresponding problem. Using Multiple EKFs allows us not only to optimally estimate the target location, but also to use the information as one of the criteria to evaluate the tracking performance. The MAV utilizes performance-based criteria that determine whether or not to initiate a maneuver such as hover or land over/on the target. The performance criteria are closed in the loop which allows the system to determine at any time whether or not to continue with the maneuver. For Vision-aided Inertial Navigation System (V-INS), a corner Harris algorithm finds the feature points, and we track them using the statistical knowledge. The feature point locations are integrated in Bierman Thornton extended Kalman Filter (BTEKF) with Inertial Measurement Unit (IMU) and sonar sensor outputs to generate vehicle states: position, velocity, attitude, accelerometer and gyroscope biases. A 6degrees-of-freedom quadrotor flight simulator is developed to test the suggested method. This paper provides the simulation results of the vision-based maneuvers: hovering over the target, and landing on the target. In addition to the simulation results, flight tests have been conducted to show and validate the system performance. The 500 gram Georgia Tech Quadrotor (GTQ)-Mini, was used for the flight tests. All processing is done onboard the vehicle and it is able to operate without human interaction. Both of the simulation and flight test results show the effectiveness of the suggested method. This system and vehicle were used for the AHS 2015 MAV Student Challenge where the GPS-denied closed-loop target search is required. The vehicle successfully found the ground target, and landed on the desired location. This paper shares the data obtained from the competition.

Autonomous exploration and mapping of environments is an important problem in robotics. Efficient exploration of structured environments requires that the robot utilize region-specific exploration strategies and coordinate with search other agents. This paper details the exploration and guidance system of a multi-quadrotor unmanned aerial system (UAS) capable of exploring cluttered indoor areas without relying on any external aides. Specifically, a graph-based frontier search algorithm which is aided by an onboard Simultaneous Localization and Mapping (SLAM) system is developed and flight tested. A technique is developed in for segmenting an indoor office-like environment into regions and to utilize the SLAM map to conduct specific activities in these regions. A goal-directed exploration strategy is created building on existing hybrid deliberative-reactive approaches to exploration. An obstacle avoidance and guidance system is implemented to ensure that the vehicle explores maximum indoor area while avoiding obstacles. The environment is explored and regions are segmented by detecting rooms and hallways which expedites the search. The multi-vehicle system is Georgia Tech Aerial Robotic Team's entry for the annual International Aerial Robotics Competition (IARC).

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