Pushbroom Stereo for High-Speed Navigation in Cluttered Environments

We present a novel stereo vision algorithm that is capable of obstacle detection on a mobile-CPU processor at 120 frames per second. Our system performs a subset of standard block-matching stereo processing, searching only for obstacles at a single depth. By using an onboard IMU and state-estimator, we can recover the position of obstacles at all other depths, building and updating a full depth-map at framerate. Here, we describe both the algorithm and our implementation on a high-speed, small UAV, flying at over 20 MPH (9 m/s) close to obstacles. The system requires no external sensing or computation and is, to the best of our knowledge, the first high-framerate stereo detection system running onboard a small UAV

X-COPTER STUDIO

We present a project that aggregates various existing robotic software and serves as a platform to conveniently control a quadrocopter, mainly for research or educational purposes. User interface runs in a browser and other components are also made with portability in mind. We provide a common interface that unifies different quadrocopter models and we implemented it for the Parrot AR.Drone 2.0. The platform is data oriented, i.e., it is based on dataflow between user objects. We implemented several such objects for: data recording and replaying, inertial and visual localization and following a given path

Distributed control and navigation system for quadrotor UAVs in GPS-denied environments

The problem of developing distributed control and navigation system for quadrotor UAVs operating in GPS-denied environments is addressed in the paper. Cooperative navigation, marker detection and mapping task solved by a team of multiple unmanned aerial vehicles is chosen as demo example. Developed intelligent control system complies with on 4D\RCS reference model and its implementation is based on ROS framework. Custom implementation of EKF-based map building algorithm is used to solve marker detection and map building task.Comment: Camera-ready as submitted (and accepted) to the 7th IEEE International Conference Intelligent Systems IS'2014, September 24-26, 2014, Warsaw, Polan

A System for the Design and Development of Vision-based Multi-robot Quadrotor Swarms

This paper presents a cost-effective framework for the prototyping of vision-based quadrotor multi-robot systems, which core characteristics are: modularity, compatibility with different platforms and being flight-proven. The framework is fully operative, which is shown in the paper through simulations and real flight tests of up to 5 drones, and was demonstrated with the participation in an international micro-aerial vehicles competition3 where it was awarded with the First Prize in the Indoors Autonomy Challenge. The motivation of this framework is to allow the developers to focus on their own research by decoupling the development of dependent modules, leading to a more cost-effective progress in the project. The basic instance of the framework that we propose, which is flight-proven with the cost-efficient and reliable platform Parrot AR Drone 2.0 and is open-source, includes several modules that can be reused and modified, such as: a basic sequential mission planner, a basic 2D trajectory planner, an odometry state estimator, localization and mapping modules which obtain absolute position measurements using visual markers, a trajectory controller and a visualization module

Computer Vision Based Object Detection and Tracking in Micro Aerial Vehicles

­­­­The ultimate goal of Computer Vision is to instruct a computer to understand and interpret visual signals and images in real time and to instruct a computer to react to the environment around them. In this work, we describe a system that allows a micro aerial vehicle (MAV), equipped with an onboard camera, to detect and track a moving target object. In an alternative implementation, the MAV instead searches the environment for the target object and flies to it. Due to the limited capability of the drone’s integrated processor, image processing is performed by a ground-based computer that also determines the necessary flight corrections and communicates them to the vehicle. The complete system, comprised of the MAV, off-board computer, and software, operates autonomously, a necessary condition for many of the applications for which such systems may be useful

Obstacle avoidance and distance measurement for unmanned aerial vehicles using monocular vision

Unmanned Aerial Vehicles or commonly known as drones are better suited for "dull, dirty, or dangerous" missions than manned aircraft. The drone can be either remotely controlled or it can travel as per predefined path using complex automation algorithm built during its development. In general, Unmanned Aerial Vehicle (UAV) is the combination of Drone in the air and control system on the ground. Design of an UAV means integrating hardware, software, sensors, actuators, communication systems and payloads into a single unit for the application involved. To make it completely autonomous, the most challenging problem faced by UAVs is obstacle avoidance. In this paper, a novel method to detect frontal obstacles using monocular camera is proposed. Computer Vision algorithms like Scale Invariant Feature Transform (SIFT) and Speeded Up Robust Feature (SURF) are used to detect frontal obstacles and then distance of the obstacle from camera is calculated. To meet the defined objectives, designed system is tested with self-developed videos which are captured by DJI Phantom 4 pro