Yucong Lin

Road detection from aerial imagery

We present a fast, robust road detection algorithm for aerial images taken from an Unmanned Aerial Vehicle. A histogram-based adaptive threshold algorithm is used to detect possible road regions in an image. A probabilistic hough transform based line segment detection combined with a clustering method is implemented to further extract the road. The proposed algorithm has been extensively tested on desert and urban images obtained using an Unmanned Aerial Vehicle. Our results indicate that we are able to successfully and accurately detect roads in 97% of the images. We experimentally validated our algorithm on over ten thousand (10,000) aerial images obtained using our UAV. These images consist of intersecting roads, bifurcating roads and roundabouts in various conditions with significant changes in lighting and intensity. Our algorithm is able to successfully detect single roads effectively in almost all the images. It is also able to detect at least one road in over 95% of the images containing bifurcating or intersecting roads.

Road Detection and Tracking from Aerial Desert Imagery

We present a fast, robust road detection and tracking algorithm for aerial images taken from an Unmanned Aerial Vehicle. A histogram-based adaptive threshold algorithm is used to detect possible road regions in an image. A probabilistic hough transform based line segment detection combined with a clustering method is implemented to further extract the road. The proposed algorithm has been extensively tested on desert images obtained using an Unmanned Aerial Vehicle. Our results indicate that we are able to successfully and accurately detect roads in 96% of the images. We experimentally validated our algorithm on over a thousand aerial images obtained using our UAV. These images consist of straight and curved roads in various conditions with significant changes in lighting and intensity. We have also developed a road-tracking algorithm that searches a local rectangular area in successive images. Initial results are presented that shows the efficacy and the robustness of this algorithm. Using this road tracking algorithm we are able to further improve the road detection and achieve a 98% accuracy.

Sense and avoid for Unmanned Aerial Vehicles using ADS-B

We present the design and implementation of an aircraft collision avoidance algorithm for Unmanned Aerial Vehicles (UAVs). Automatic Dependent Surveillance-Broadcast (ADS-B) is used to detect aircraft. The UAV needs to fly through pre-assigned waypoints while avoiding collisions with other aircraft. The aircraft are indifferent to the UAV. A collision with aircraft are detected by simulating the UAVs trajectory along the path of assigned waypoints using its closed-loop dynamics. A sampling based algorithm is used for collision avoidance path planning. A second collision check is performed on the generated path with the updated UAV and aircrafts states. The path will be re-planned if it leads to a collision. The algorithm was validated in Software-In-the-Loop Simulation (SITL). ADS-B data obtained from commercial aircraft flying over the Phoenix Skyharbor airport were used for simulating the collisions. The paper shows that the algorithm enables the UAV to avoid multiple aircraft with different approaching angles and speeds.

Sampling-Based Path Planning for UAV Collision Avoidance

The ability to avoid collisions with moving obstacles, such as commercial aircraft is critical to the safe operation of unmanned aerial vehicles (UAVs) and other air traffic. This paper presents the design and implementation of sampling-based path planning methods for a UAV to avoid collision with commercial aircraft and other moving obstacles. In detail, the authors develop and demonstrate a method based on the closed-loop rapidly-exploring random tree algorithm and three variations of it. The variations are: 1) simplification of trajectory generation strategy; 2) utilization of intermediate waypoints; 3) collision prediction using reachable set. The methods were validated in software-in-the-loop simulations, hardware-in-the-loop simulations, and real flight experiments. It is shown that the algorithms are able to generate collision free paths in real time for the different types of UAVs among moving obstacles of different numbers, approaching angles, and speeds.

Obstacle Detection System for Small UAVs using ADS-B and Thermal Imaging.

Thermal Infrared (TIR) imaging is a promising technology which can provide enhanced capabilities to current vision-based Sense-and-Avoid (SAA) systems. It allows operation under extreme illumination conditions, such as direct sun exposure and during nighttime. This paper presents a lightweight obstacle detection system for small UAVs that integrates a TIR camera and an Automatic Dependent Surveillance Broadcast (ADS-B) receiver. Algorithms for the detection of flying obstacles in TIR images were developed and TIR images were experimentally compared with synchronized RGB images for validation. Matching between aircraft detected in TIR images and those reported by an ADS-B receiver was performed to obtain distance information to the visually detected aircraft. We experimentally proved that our system is able to enhance individual ADS-B and TIR detection capabilities by detecting aircraft under challenging illumination conditions at real-time frame rates while providing distance estimations to visual detections.

Sampling based collision avoidance for UAVs

We present the design and implementation of a path planning algorithm for Unmanned Aerial Vehicles (UAVs) to avoid collisions with other aircraft. The aircraft are indifferent to the UAV. A sampling based method is developed to generate the avoidance path. The UAVs closed-loop system is used to simulate the trajectory in collision check used in path planning. The generated path is then checked against updated states of the UAV and obstacle aircraft, and a new path will be planned if the original one leads to a collision. The algorithm is validated in Hardware-In-the-Loop simulation (HIL) and real flight experiments. The paper shows that the algorithm is able to generate paths for the UAV to avoid obstacles of different numbers, approaching angles, and speeds.

Autonomous detection of volcanic plumes on outer planetary bodies

We experimentally evaluated the efficacy of various autonomous supervised classification techniques for detecting transient geophysical phenomena. We demonstrated methods of detecting volcanic plumes on the planetary satellites Io and Enceladus using spacecraft images from the Voyager, Galileo, New Horizons, and Cassini missions. We successfully detected 73-95% of known plumes in images from all four mission datasets.Additionally, we showed that the same techniques are applicable to differentiating geologic features, such as plumes and mountains, which exhibit similar appearances in images.