A real-time dynamic obstacle tracking and mapping system for UAV navigation and collision avoidance with an RGB-D camera

The real-time dynamic environment perception has become vital for autonomous robots in crowded spaces. Although the popular voxel-based mapping methods can efficiently represent 3D obstacles with arbitrarily complex shapes, they can hardly distinguish between static and dynamic obstacles, leading to the limited performance of obstacle avoidance. While plenty of sophisticated learning-based dynamic obstacle detection algorithms exist in autonomous driving, the quadcopter's limited computation resources cannot achieve real-time performance using those approaches. To address these issues, we propose a real-time dynamic obstacle tracking and mapping system for quadcopter obstacle avoidance using an RGB-D camera. The proposed system first utilizes a depth image with an occupancy voxel map to generate potential dynamic obstacle regions as proposals. With the obstacle region proposals, the Kalman filter and our continuity filter are applied to track each dynamic obstacle. Finally, the environment-aware trajectory prediction method is proposed based on the Markov chain using the states of tracked dynamic obstacles. We implemented the proposed system with our custom quadcopter and navigation planner. The simulation and physical experiments show that our methods can successfully track and represent obstacles in dynamic environments in real-time and safely avoid obstacles

Proceedings of the International Micro Air Vehicles Conference and Flight Competition 2017 (IMAV 2017)

The IMAV 2017 conference has been held at ISAE-SUPAERO, Toulouse, France from Sept. 18 to Sept. 21, 2017. More than 250 participants coming from 30 different countries worldwide have presented their latest research activities in the field of drones. 38 papers have been presented during the conference including various topics such as Aerodynamics, Aeroacoustics, Propulsion, Autopilots, Sensors, Communication systems, Mission planning techniques, Artificial Intelligence, Human-machine cooperation as applied to drones

UAV or Drones for Remote Sensing Applications in GPS/GNSS Enabled and GPS/GNSS Denied Environments

The design of novel UAV systems and the use of UAV platforms integrated with robotic sensing and imaging techniques, as well as the development of processing workflows and the capacity of ultra-high temporal and spatial resolution data, have enabled a rapid uptake of UAVs and drones across several industries and application domains.This book provides a forum for high-quality peer-reviewed papers that broaden awareness and understanding of single- and multiple-UAV developments for remote sensing applications, and associated developments in sensor technology, data processing and communications, and UAV system design and sensing capabilities in GPS-enabled and, more broadly, Global Navigation Satellite System (GNSS)-enabled and GPS/GNSS-denied environments.Contributions include:UAV-based photogrammetry, laser scanning, multispectral imaging, hyperspectral imaging, and thermal imaging;UAV sensor applications; spatial ecology; pest detection; reef; forestry; volcanology; precision agriculture wildlife species tracking; search and rescue; target tracking; atmosphere monitoring; chemical, biological, and natural disaster phenomena; fire prevention, flood prevention; volcanic monitoring; pollution monitoring; microclimates; and land use;Wildlife and target detection and recognition from UAV imagery using deep learning and machine learning techniques;UAV-based change detection

Towards UAV-assisted monitoring of onshore geological CO2 storage site

Scientists all over the world look for solutions to reduce greenhouse gas emissions in an effort to achieve proclaimed emissions reduction targets. An intriguing candidate with the potential to make a substantial contribution to this attempt is carbon capture and storage (CCS). The key advantage of CCS is that it provides the possibility to make a significant impact on the reduction of anthropogenic carbon dioxide (CO2) emissions from power plants and carbon-rich industry processes while maintaining existing fossil fuel energy infrastructure. The technique could therefore be used as a transitional solution until fossil fuels can be eliminated from the energy generation mix, and the energy efficiency of industrial processes as well as appliances and products is further improved. Like other technologies, CCS comes with its risks and rewards. To minimize possible negative impacts on humans as well as on the environment, it is necessary to understand the risks and to address them accordingly. A range of monitoring solutions for geological CO2 storage sites is available. However, a cost-effective solution for the regular observation of atmospheric CO2 concentrations (or tracer concentrations) of large areas above onshore geological CO2 storage sites has yet to be developed. This thesis discusses the use of a helicopter unmanned aerial vehicle (UAV) to fill this gap. The robot platform and its autopilot are designed to cope with ongoing sensor developments in addition to providing safety features necessary for the beyond line-of-sight operation of the UAV. The design focuses on the use of commercial off-the-shelf components for the aerial platform in order to shorten the development time and to reduce costs. The autopilot does neither enforce a specific helicopter model nor defines a set position estimation unit to be used. Access to the control loop enables low-level extensions like obstacle avoidance to be implemented. The developed solution allows the monitoring of an area of approximately 750m2 with one set of batteries in one altitude with a spatial resolution of 2m by 2m. Experiments show that point source leaks of as low as 100kg CO2 per day can be detected and their source located. As opposed to autonomous take-offs of the helicopter UAV, autonomous landings on small dedicated helipads require an accurate localization system. A time difference of arrival (TDOA) based acoustic localization system which is based on planar microphone arrays with at least four microphones is proposed. The system can be embedded into the landing platform and provides the accuracy necessary to land the UAV on a helipad of the size of 1m by 1m. A review of existing TDOA-based approaches is given. Simulations show that the developed approach outperforms its direct competitors for the targeted task. Furthermore, experimental results with the developed UAV confirm the feasibility of the introduced method. The effects of the sensor arrangement onto the quality of the calculated position estimates are also discussed. In order to combine robotic-assisted monitoring solutions and other monitoring strategies (e.g. sensor networks and individual sensors) into a single solution, it is necessary to have a framework which allows next to the measurement data analysis also the management (path changes, robot behavior changes, monitoring of internal robot state) of possibly multiple heterogeneous mobile robotic systems. A modular user interface (UI) framework is proposed which allows robots from different vendors and with various configurations next to individual sensors and sensor networks to be managed from a single application. The software system introduces a strict separation between the robot control software and UIs. UI implementations inside the UI framework can be reused across robot platforms, which can reduce the integration time of new robots significantly. The end user benefits by being able to manage a fleet of robots from various vendors and being able to analyze all the measurement data together in a single solution