Simplified Building Models Extraction From Ultra-Light UAV Imagery

Generating detailed simplified building models such as the ones present on Google Earth is often a difficult and lengthy manual task, requiring advanced CAD software and a combination of ground imagery, LIDAR data and blueprints. Nowadays, UAVs such as the Falcon 8 have reached the maturity to offer an affordable, fast and easy way to capture large amounts of oblique images covering all part of a building. In this paper we present a state-of-the-art photogrammetry and visual reconstruction pipeline provided by Pix4D applied to medium resolution imagery acquired by such UAVs. The key element of simplified building models extraction is the seamless integration of the outputs of such a pipeline for a final manual refinement step in order to minimize the amount of manual wor

Iuliu Vasilescu Carrick Detweiler AMOUR V: A Hovering Energy Efficient Underwater Robot Capable of Dynamic Payloads

Abstract In this paper we describe the design and control algorithms o

Energy-efficient Autonomous Four-rotor Flying Robot Controlled at 1 kHz

Abstract—We describe an efficient, reliable, and robust four-rotor flying platform for indoor and outdoor navigation. Cur-rently, similar platforms are controlled at low frequencies due to hardware and software limitations. This causes uncertainty in position control and instable behavior during fast maneuvers. Our flying platform offers a 1 kHz control frequency and motor update rate, in combination with powerful brushless DC motors in a light-weight package. Following a minimalistic design approach this system is based on a small number of low-cost components. Its robust performance is achieved by using simple but reliable highly optimized algorithms. The robot is small, light, and can carry payloads of up to 350g. I

Co-ordinated Tracking and Planning Using Air and Ground Vehicles

The MAV ’08 competition in Agra, India focused on the problem of using air and ground vehicles to locate and rescue hostages being held in a remote building. Executing this mission required addressing a number of technical challenges. The first such technical challenge was the design and operation of a micro air vehicle (MAV) capable of flying the necessary distance and carrying a sensor payload for localizing the hostages. The second technical challenge was the design and implementation of vision and state estimation algorithms to detect and track ground adversaries guarding the hostages. The third technical challenge was the design and implementation of robust planning algorithms that could co-ordinate with the MAV state estimates and generate tactical motion plans for ground vehicles to reach the hostage location without detection by the ground adversaries. In this paper we describe our solutions to these challenges. Firstly, we summarize the design of our micro air vehicle, focusing on the navigation and sensing payload. Secondly, we describe the vision and state estimation algorithms used to track ground features through a sequence of images from the MAV, including stationary obstacles and moving adversaries. Thirdly, we describe the planning algorithm used to generate motion plans to allow the ground vehicles to approach the hostage building undetected by adversaries tracked from the air. Finally, we provide results of our system’s performance during the mission execution

On the Design and Use of a Micro Air Vehicle to Track and Avoid Adversaries

The MAV ’08 competition focused on the problem of using air and ground vehicles to locate and rescue hostages being held in a remote building. To execute this mission, a number of technical challenges were addressed, including designing the micro air vehicle (MAV), using the MAV to geo-locate ground targets, and planning the motion of ground vehicles to reach the hostage location without detection. In this paper, we describe the complete system designed for the MAV ’08 competition, and present our solutions to three technical challenges that were addressed within this system. First, we summarize the design of our micro air vehicle, focusing on the navigation and sensing payload. Second, we describe the vision and state estimation algorithms used to track ground features, including stationary obstacles and moving adversaries, from a sequence of images collected by the MAV. Third, we describe the planning algorithm used to generate motion plans for the ground vehicles to approach the hostage building undetected by adversaries; these adversaries are tracked by the MAV from the air. We examine different variants of a search algorithm and describe their performance under different conditions. Finally, we provide results of our system’s performance during the mission execution.United States. Army Research Office (MAST CTA)Singapore. Armed ForcesUnited States. Air Force Office of Scientific Research (contract # F9550-06-C-0088)Aurora Flight Sciences Corp.Boeing CompanyNational Energy Research Scientific Computing Center (U.S.)National Science Foundation (U.S.). Division of Information and Intelligent Systems (grant # 0546467)Massachusetts Institute of Technology. Air Vehicle Research Center (MAVRC

SFly: Swarm of micro flying robots

The SFly project is an EU-funded project, with the goal to create a swarm of autonomous vision controlled micro aerial vehicles. The mission in mind is that a swarm of MAV's autonomously maps out an unknown environment, computes optimal surveillance positions and places the MAV's there and then locates radio beacons in this environment. The scope of the work includes contributions on multiple different levels ranging from theoretical foundations to hardware design and embedded programming. One of the contributions is the development of a new MAV, a hexacopter, equipped with enough processing power for onboard computer vision. A major contribution is the development of monocular visual SLAM that runs in real-time onboard of the MAV. The visual SLAM results are fused with IMU measurements and are used to stabilize and control the MAV. This enables autonomous flight of the MAV, without the need of a data link to a ground station. Within this scope novel analytical solutions for fusing IMU and vision measurements have been derived. In addition to the realtime local SLAM, an offline dense mapping process has been developed. For this the MAV's are equipped with a payload of a stereo camera system. The dense environment map is used to compute optimal surveillance positions for a swarm of MAV's. For this an optimiziation technique based on cognitive adaptive optimization has been developed. Finally, the MAV's have been equipped with radio transceivers and a method has been developed to locate radio beacons in the observed environment