Franz Andert

An Unmanned Helicopter for Autonomous Flights in Urban Terrain

This work summarizes a multi-disciplinary research project, focusing on key enabling techniques towards true autonomous flight of small, low flying VTOL UAVs. Research activities cover the flying testbed, a simulation and testing environment, as well as integrated components for onboard navigation, perception, planning and control. Promising results and feasibility demonstrations in flight tests underline the successful domain specific enhancements of approaches based on aeronautical engineering, computer science and mobile robotics. The current approaches pave the way towards further research in improved flight control performance and more system autonomy when a-priori mission uncertainties increase.

Drawing stereo disparity images into occupancy grids: Measurement model and fast implementation

Mapping the environment is necessary for navigation in unknown areas with autonomous vehicles. In this context, a method to process depth images for occupancy grid mapping is developed. Input data are images with pixel-based distance information and the corresponding camera poses. A measurement model, focusing on stereo-based depth images and their characteristics, is presented. Since an enormous amount of range data must be processed, improvements like image pyramids are used so that the image analysis is possible in real-time. Output is a grid-based image interpretation for sensor fusion, i.e. a world-centric occupancy probability array containing information stored in a single image. Different approaches to draw pixel information into a grid map are presented and discussed in terms of accuracy and performance. As a final result, 3D occupancy grids from aerial image sequences are presented.

Autonomous Vision-Based Helicopter Flights Through Obstacle Gates

The challenge for unmanned aerial vehicles to sense and avoid obstacles becomes even harder if narrow passages have to be crossed. An approach to solve a mission scenario that tackles the problem of such narrow passages is presented here. The task is to fly an unmanned helicopter autonomously through a course with gates that are only slightly larger than the vehicle itself. A camera is installed on the vehicle to detect the gates. Using vehicle localization data from a navigation solution, camera alignment and global gate positions are estimated simultaneously. The presented algorithm calculates the desired target waypoints to fly through the gates. Furthermore, the paper presents a mission execution plan that instructs the vehicle to search for a gate, to fly through it after successful detection, and to search for a proceeding one. All algorithms are designed to run onboard the vehicle so that no interaction with the ground control station is necessary, making the vehicle completely autonomous. To develop and optimize algorithms, and to prove the correctness and accuracy of vision-based gate detection under real operational conditions, gate positions are searched in images taken from manual helicopter flights. Afterwards, the integration of visual sensing and mission control is proven. The paper presents results from full autonomous flight where the helicopter searches and flies through a gate without operator actions.

Mapping and path planning in complex environments: An obstacle avoidance approach for an unmanned helicopter

This paper presents an obstacle avoidance method that is performed with an unmanned helicopter. The approach begins with a mapping step where information from sensor data about previously unknown dangers is extracted into an occupancy grid and eventually converted into a polygonal 3D world model. This continuously updating map is used by a path planner that generates and updates a 3D trajectory guiding the vehicle through safe passages around the detected objects. The algorithms are generic but optimized for unmanned aircraft and a stereo camera as the environmental sensor. Computation is fully executed on board so that a ground control station is only needed for supervision. With successful obstacle detection and avoidance flight tests, the paper shows the qualification of the presented method under real operational conditions.

Rapid Multi-Query Path Planning For A Vertical Take-Off and Landing Unmanned Aerial Vehicle

This paper presents an online approach for multi-query path planning that enables an unmanned aircraft to quickly react to newly located or changed obstacles. All three spatial dimensions are considered during planning, as it is likely to be necessary for urban terrain with overhangs or inside buildings.Asampling-based motion planning approach is combined with an anytime path search such that an online trade-off between path optimality and rapid path computation is possible. The presented global path planner can rapidly handle global cost changes acquired during flight. Moreover, the multi-query property of the planning system supports combinatorial optimization problems such that the decisional autonomy is maximized during flight. This increase in system autonomy is achieved for holonomic maneuvering through scenes with unforeseen obstacle changes. This way, a common constraining requirement for unmanned aircraft can be relaxed: with onboard sensing and planning capabilities, a highly reliable, real-time remote control link is not necessary to ensure collision-free flight and higher level decision-making.

A fast and small 3-D Obstacle model for autonomous applications

This paper presents a mapping process that can be used for autonomous applications like obstacle avoidance and trajectory planning. The process is real-time capable, and works in full 3-D environments. The mapping starts with building an occupancy grid out of sensor data. Within this grid, single objects are recognized and their polygonal shapes are calculated. The model for object shape representation is rather rough and uses only right prisms and horizontal floor planes. This makes the shape calculation very fast. The extracted objects are not complex so that an external application will be fast as well. As a special application, the approach is tested on an aerial vehicle using a stereo camera system.

On the safe navigation problem for unmanned aircraft: Visual odometry and alignment optimizations for UAV positioning

With increasing automation of unmanned aircraft and the endeavor to fly between buildings in cities and in other occluded areas, safe navigation is essentially required but still a challenge. This paper is about the important issue of vehicle positioning in the case of satellite signal dropouts, and it presents a visual odometry method to compensate GPS positioning interruptions. The presented approach follows common triangulation principles and nonlinear optimization methods. Absolute scale is obtained by a stereo camera, although stereo is required only from time to time. In addition to the estimation of the vehicle position, the method estimates the camera alignment with respect to the vehicle, yielding a more accurate map and pose estimation. Returned vehicle poses are available in real-time with high update rates, being ready for an integration into state estimation and flight control. To demonstrate the algorithm properties, the paper incloses the evaluation of sensor data from unmanned helicopter flight tests. It shows the successful bridging of satellite positioning gaps by calculating the vehicle trajectory only by vision. Finally, the paper discusses some open issues for future work.

Combined grid and feature-based occupancy map building in large outdoor environments

This paper presents an approach to create three-dimensional occupancy maps from an aerial vehicle with stereo vision. The main idea is to create an occupancy grid that moves along with the vehicle and extract features into a fixed global map. Vice versa, global features or a-priori knowledge can be inserted into the grid. The maps are calculated onboard to be used for autonomous behavior like path planning and obstacle avoidance. With the described method, maps are created and updated in real-time, and due to its flexibility, the vehicle is not restricted to a pre-defined area. The developed approach has been demonstrated in flights with a small unmanned helicopter.

Lidar-Aided Camera Feature Tracking and Visual SLAM for Spacecraft Low-Orbit Navigation and Planetary Landing

This paper explores the state estimation problem for an autonomous precision landing approach on celestial bodies. This is generally based on sensor fusion from inertial and optical sensor data. Independent of the state estimation filter, a remaining problem is the provision of position updates without the use of known absolute support information as it appears when the vehicle navigates within unknown terrain. Visual odometry or simultaneous localization and mapping (SLAM) approaches typically provide relative position. This is quite suitable, but it can be adverse due to error accumulation. The presented method combines monocular camera images with laser distance measurements to allow visual SLAM without errors from increasing scale uncertainty. It is shown that this reduces the accumulated error in comparison to sole monocular visual SLAM. Further, the presented method integrates the matching to known landmarks if they are available in the beginning of a landing approach so that the relative optical navigation can be initialized without systematic errors. Finally, tests with a simulated moon landing are performed and it is shown that the method is capable of navigating down to the ground impact.

Onboard Mission Management for a VTOL UAV Using Sequence and Supervisory Control

This chapter addresses the challenges of onboard mission management for small, low flying UAVs in order to reduce their dependency on reliable remote control. The system presented and tested onboard an unmanned aerial vehicle (UAV) provides levels of autonomy, scalable at runtime either by the operator or due to the absence of a data link. This way, it is a feasible approach towards autonomous flight guidance within the low-altitude domain (e.g. urban areas) where unpredictable events are likely to require onboard decision-making. In the following sections the problems of onboard mission management, embedded high level architectures and their implementation issues are discussed. The design of a onboard Mission Management System for a test platform with vertical take-off and landing (VTOL) capabilities is presented, followed by discussions of the implemented system and a research outlook.