Sungsik Huh

Integrated navigation system using camera and gimbaled laser scanner for indoor and outdoor autonomous flight of UAVs

This paper describes an integrated navigation sensor module, including a camera, a laser scanner, and an inertial sensor, for unmanned aerial vehicles (UAVs) to fly both indoors and outdoors. The camera and the gimbaled laser sensor work in a complementary manner to extract feature points from the environment around the vehicle. The features are processed using an online extended Kalman filter (EKF) in simultaneous localization and mapping (SLAM) algorithm to estimate the navigational states of the vehicle. In this paper, a new method is proposed for calibrating a camera and a gimbaled laser sensor. This calibration method uses a simple visual marker to calibrate the camera and the laser scanner with each other. We also propose a real-time navigation algorithm based on the EKF SLAM algorithm, which is suitable for our camera-laser sensor package. The algorithm merges image features with laser range data for state estimation. Finally, these sensors and algorithms are implemented on our octo-rotor UAV platform and the result shows that our onboard navigation module can provide a real-time three-dimensional navigation solution without any assumptions or prior information on the surroundings.

A Vision-Based Automatic Landing Method for Fixed-Wing UAVs

In this paper, a vision-based landing system for small-size fixed-wing unmanned aerial vehicles (UAVs) is presented. Since a single GPS without a differential correction typically provide position accuracy of at most a few meters, an airplane equipped with a single GPS only is not guaranteed to land at a designated location with a sufficient accuracy. Therefore, a visual servoing algorithm is proposed to improve the accuracy of landing. In this scheme, the airplane is controlled to fly into the visual marker by directly feeding back the pitch and yaw deviation angles sensed by the forward-looking camera during the terminal landing phase. The visual marker is a monotone hemispherical airbag, which serves as the arresting device while providing a strong and yet passive visual cue for the vision system. The airbag is detected by using color- and moment-based target detection methods. The proposed idea was tested in a series of experiments using a blended wing-body airplane and proven to be viable for landing of small fixed-wing UAVs.

Indoor UAV Control Using Multi-Camera Visual Feedback

This paper presents the control of an indoor unmanned aerial vehicle (UAV) using multi-camera visual feedback. For the autonomous flight of the indoor UAV, instead of using onboard sensor information, visual feedback concept is employed by the development of an indoor flight test-bed. The indoor test-bed consists of four major components: the multi-camera system, ground computer, onboard color marker set, and quad-rotor UAV. Since the onboard markers are attached to the pre-defined location, position and attitude of the UAV can be estimated by marker detection algorithm and triangulation method. Additionally, this study introduces a filter algorithm to obtain the full 6-degree of freedom (DOF) pose estimation including velocities and angular rates. The filter algorithm also enhances the performance of the vision system by making up for the weakness of low cost cameras such as poor resolution and large noise. Moreover, for the pose estimation of multiple vehicles, data association algorithm using the geometric relation between cameras is proposed in this paper. The control system is designed based on the classical proportional-integral-derivative (PID) control, which uses the position, velocity and attitude from the vision system and the angular rate from the rate gyro sensor. This paper concludes with both ground and flight test results illustrating the performance and properties of the proposed indoor flight test-bed and the control system using the multi-camera visual feedback.

Vision-Based Detection and Tracking of Airborne Obstacles in a Cluttered Environment

This paper proposes an image processing algorithm for ‘sense-and-avoid’ of aerial vehicles in short-range at low altitude and shows flight experiment results. Since it can suppress the negative effects cause cluttered environment in image sequence such as the ground seen or sensitivity of threshold value during low-altitude flight, proposed algorithm has better performance of collision avoidance. Furthermore, proposed algorithm can perform better than simple color-based detection and tracking methods because it takes the characteristics of vehicle dynamics into account in image plane. The performance of proposed algorithm is validated by post image processing using video clip taken from flight test and actual flight test with simple avoidance maneuver.

An image processing algorithm for detection and tracking of aerial vehicles

This paper proposes an image processing algorithm for detection and tracking of aerial vehicles in sight. The proposed algorithm detects moving objects using the image homography calculated from a video stream taken from the onboard camera and determines whether the detected objects are approaching aerial vehicles by the Probabilistic Multi- Hypothesis Tracking (PMHT) method. This algorithm performs well especially when it is needed to detect any approaching aircraft seen with cluttered background. Further, our algorithm is suitable for real flight application as it is less sensitive to light conditions or color variations. The performance of the proposed algorithm is validated by applying it to the onboard video clips taken during actual flights using two unmanned aerial vehicles.

An Image Processing Algorithm for Detection and Tracking of Aerial Vehicles in Short-Range

This paper proposes an image processing algorithms for detection and tracking of aerial vehicles in short-range. Proposed algorithm detects moving objects by using image homography calculated from consecutive video frames and determines whether the detected objects are approaching aerial vehicles by the Probabilistic Multi-Hypothesis Tracking method(PMHT). This algorithm can perform better than simple color-based detection methods since it can detect moving objects under complex background such as the ground seen during low altitude flight and consider the characteristics of vehicle dynamics. Furthermore, it is effective for the flight test due to the reduction of thresholding sensitivity against external factors. The performance of proposed algorithm is verified by applying to the onboard video obtained by flight test.

Experimental Framework for Controller Design of a Rotorcraft Unmanned Aerial Vehicle Using Multi-Camera System

This paper describes the experimental framework for the control system design and validation of a rotorcraft unmanned aerial vehicle (UAV). Our approach follows the general procedure of nonlinear modeling, linear controller design, nonlinear simulation and flight test but uses an indoor-installed multi-camera system, which can provide full 6-degree of freedom (DOF) navigation information with high accuracy, to overcome the limitation of an outdoor flight experiment. In addition, a 3-DOF flying mill is used for the performance validation of the attitude control, which considers the characteristics of the multi-rotor type rotorcraft UAV. Our framework is applied to the design and mathematical modeling of the control system for a quad-rotor UAV, which was selected as the test-bed vehicle, and the controller design using the classical proportional-integral-derivative control method is explained. The experimental results showed that the proposed approach can be viewed as a successful tool in developing the controller of new rotorcraft UAVs with reduced cost and time.

Multiple UAVs tracking algorithm with a multi-camera system

This paper presents the tracking and estimation of indoor UAVs using multi-camera visual feedback. The proposed vision algorithm enhances the performance of the vision system by making up for a weakness of low cost camera such as poor resolution and large noise. For the pose estimation of multiple vehicles, data association algorithm using the geometric relation between cameras is employed in this paper.

Vision-based sense-and-avoid framework for unmanned aerial vehicles

This paper describes a vision-based sense-and-avoid framework to detect approaching aircraft especially observed with cluttered background. The proposed framework consists of a vision system with a camera that processes the incoming images using a series of algorithms in real time to isolate moving aerial objects on the image plane and classify them using a particle filter. Once an approaching aerial object has been detected on a potential collision course, the aircraft performs an evasive maneuver. The performance of the proposed sense-and-avoid algorithm is validated in a series of test flights using two unmanned aerial vehicles.

A Vision-based Automatic Landing System for Fixed-wing UAVs using an Inflated Airbag

We present a vision-based landing system for fixed-wing unmanned aerial vehicles (UAVs) using an inflated airbag. As the statistics show, the landing stage is identified as most accident-prone among the entire flight regimes due to the complicated dynamics, difficult control characteristics and its dependency on many external factors such as wind conditions. Therefore there have been many attempts to automate the landing process on conventional runways or external arresting mechanisms such as ropes or nets. In this paper, we propose a relatively simple yet highly effective vision-based landing method using an inflatable airbag. Its distinctive color provides unmistakably strong yet passive visual cue for the onboard vision system, which runs a fast and robust color tracking algorithm for geolocation and visual servoing. The airbag has a dome shape so that the airplane can approach from any direction, unlike the net-based approach that may suffer from crosswind. The proposed idea has been validated in a series of experiments using a blended wing-body (BWB) UAV testbed. Experiment results show that the proposed method is a viable approach for field applications without the need of expensive aiding systems. I. Introduction