This paper presents an approach on how to integrate pan-tilt camera systems into the state estimation of unmanned aircraft to introduce an additional source of navigation data. The proposed state estimation algorithm based on an Unscented Kalman Filter is presented with an additional augmentation of the measurement model to cope for the uncertainties introduced by the pan-tilt unit of the camera system. Furthermore, the correlation of the visual measurements due to the common pan and tilt axes of the camera system is addressed. Second, the advantages of using an actively controlled pan-tilt camera system in the context of landmark-based navigation are shown. The navigation performance of such an actively controlled camera system is compared against fixed camera systems. Furthermore, the focal length aka. zoom is taken into account to further motivate the use of those actively controlled pan-tilt camera systems. The comparison is based on a Matlab/Simulink simulation environment providing measurements like inertial, magnetic or barometric sensor data as well as emulated landmark tracking data. Finally, an outlook on future flight tests using an 85 kg unmanned helicopter is given.

The project City-ATM, launched by the German Aerospace Center DLR in 2018, aims to integrate new airspace users, such as unmanned aerial vehicles, into uncontrolled airspace. An air traffic management and traffic flow control concept were developed. The phase 1 demonstration in 2019 showed a bridge inspection featuring, amongst other elements, the flight of several drones in a limited area, beyond visual line of sight operation, strategic flight planning considering strategic geo-fences, and tactical conflict detection. To enrich the base concept from phase 1 with more U-Space services, the concept has been extended for phase 2 by dynamic geo-fencing for the tactical avoidance of danger spots. This article describes how dynamic geo-fences can be modeled around a hazard area, how the dynamic opening and closure of these fences can be distributed to relevant systems, and how the fences are considered by on-ground and already in air vehicles. The complete concept has been implemented and tested at the National Experimental Test Center in Cochstedt. Finally, the execution of successful flight trials has been described.

Since 2010 the German Aerospace Center is working on the project Autonomous Terrain-based Optical Navigation (ATON). Its objective is the development of technologies which allow autonomous navigation of spacecraft in orbit around and during landing on celestial bodies like the Moon, planets, asteroids and comets. The project developed different image processing techniques and optical navigation methods as well as sensor data fusion. The setup—which is applicable to many exploration missions—consists of an inertial measurement unit, a laser altimeter, a star tracker and one or multiple navigation cameras. In the past years, several milestones have been achieved. It started with the setup of a simulation environment including the detailed simulation of camera images. This was continued by hardware-in-the-loop tests in the Testbed for Robotic Optical Navigation (TRON) where images were generated by real cameras in a simulated downscaled lunar landing scene. Data were recorded in helicopter flight tests and post-processed in real-time to increase maturity of the algorithms and to optimize the software. Recently, two more milestones have been achieved. In late 2016, the whole navigation system setup was flying on an unmanned helicopter while processing all sensor information onboard in real time. For the latest milestone the navigation system was tested in closed-loop on the unmanned helicopter. For that purpose the ATON navigation system provided the navigation state for the guidance and control of the unmanned helicopter replacing the GPS-based standard navigation system. The paper will give an introduction to the ATON project and its concept. The methods and algorithms of ATON are briefly described. The flight test results of the latest two milestones are presented and discussed.

This paper presents the application of a rotary wing Unmanned Aerial Vehicle (UAV) as a testbed for the optical navigation system developed in the project “Autonomous Terrain-based Optical Navigation” (ATON) of the German Aerospace Center (DLR). Since using optical sensor data in navigation systems for exploration missions is a promising technology, many projects have focused on the development of such navigation systems. To allow autonomous operation of these developed navigation technologies in the targeted environment extensive testing is necessary for achieving an appropriate grade of reliability. A review of the current approaches for true-scale testing of optical navigation systems for lunar or planetary landing missions is conducted, and the proposed testing methodology is motivated. The paper describes the development steps necessary to conduct closed-loop real-time flight tests using a rotary wing UAV. This includes the sensor configuration, preparation of the flight test area and ground truth generation. Finally, the resulting navigation performance of the ATON navigation system in the closed-loop real-time flight tests is presented.

In this paper we present an approach to combine error state estimation with total state monocular simultaneous localization and mapping (SLAM) in a single Unscented Kalman Filter (UKF). The map features use the inverse depth parametrization for undelayed initialization and for the ability to use low-parallax features with unknown depth information. Furthermore, a new map feature initialization method is presented using the Unscented transform (UT). This method allows to capture all correlations between the map features and the error state variables without the necessity to calculate any Jacobian matrices.

This paper presents an optical navigation method for unmanned flights where satellite navigation might be disturbed. Core is an inertial-based navigation filter that provides high-frequent flight state updates and where satellite positioning can be replaced with updates from optical sensors in case of satellite signal dropouts. This alternative positioning is determined by a simultaneous localization and mapping (SLAM) algorithm that can generally handle 2D and 3D feature inputs from arbitrary sources. In the presented setup, 2D features are generated from camera images where 3D information from laser range is added if available. Within a simulation environment, visual SLAM is fed with emulated inputs. The architecture provides strong separation, i.e. the sensor pre-processing, visual SLAM, state estimation, and flight control modules are exchangeable and can be run and tested independently. This concept may prevent a very tight coupling of all components, but with regard to future certification, validation and verification will be easier once single components are assured. The navigation method is tested in two ways: first within a flight test of an 85-kg helicopter where only the quality of optical-aided state estimation is tested, and second within a closed-loop simulation where mutual interactions between navigation and flight control are critical in terms of stability. The tests underline the applicability of the presented approach, making this method ready for automatic camera-based flights.

This paper presents an optical-aided navigation method for automatic flights where satellite navigation might be disturbed. The proposed solution follows common approaches where satellite position updates are replaced with measurements from environment sensors such as a camera, lidar or radar as required. The alternative positioning is determined by a localization and mapping (SLAM) algorithm that handles 2D feature inputs from monocular camera images as well as 3D inputs from camera images that are augmented by range measurements. The method requires neither known landmarks nor a globally flat terrain. Beside the visual SLAM algorithm, the paper describes how to generate 3D feature inputs from lidar and radar sources and how to benefit from both monocular triangulation and 3D features. Regarding state estimation, the approach decouples visual SLAM from the filter updates. This allows software and hardware separation, i.e. visual SLAM computations on powerful hardware while the main filter can be installed on real-time hardware with possible lower capabilities. The localization quality in case of satellite dropouts is tested with data sets from manned and unmanned flights with different sensors while keeping all parameters constant. The tests show the applicability of this method in flat and hilly terrain and with different path lengths from few hundred meters to many kilometers. The relative navigation achieves an accumulation error of 1–6 % of distance traveled depending on the flight scenario. In addition to the flights, the paper discusses flight profile limitations when optical navigation methods are used.

Dieser Bericht umfasst die Beschreibung und die Ergebnisse der Studie Stabile Navigation und Gelandefolgeflug fur VTOL UAS, welche im Zeitraum 2013-2017 durchgefuhrt wurde. Zielrichtung dieser Studie ist die Untersuchung und Beschreibung von Methoden zur Steigerung der Automation im Bereich der Flugfuhrung von VTOL UAS. Dadurch kann der Operateur entlastet und z.B. in die Lage versetzt werden, mehr Kapazitat auf den Einsatz der UAV-Nutzlast verwenden zu konnen. Neben der Methodenentwicklung erfolgt die Erprobung und Validierung der neuen Verfahren auf einem unbemannten Versuchstrager.

This paper explores the state estimation problem for an autonomous precise landing approach on celestial bodies. As part of the project “Autonomous Terrain-based Optical Navigation” (ATON) of the German Aerospace Center (DLR) this paper describes the central state estimation algorithm. This algorithm combines high rate inertial navigation with low rate sensor fusion. The description includes the software architecture of the developed navigation system and the estimator, which is based on an Unscented Kalman Filter (UKF). The UKF equations are presented as well as the specific transition and observation models. Additionally, different image processing modules, providing the UKF with position updates, are described shortly. Finally, the evaluation of the implemented system based on performed flight tests imitating a landing on the Moon is presented. These tests show that the method is capable of providing a robust navigation solution during the landing approach.

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.

In this work a calibration method which is easy to use and is capable of calibrating a combination of a magnetometer and an accelerometer is described. The calibration method accounts for hard and soft iron effects created by the platform, sensor errors like biases, scale factors and non-orthogonalities as well as the relative orientation between the magnetometer and the accelerometer. The algorithm is based on a least squares problem, which is solved using a singular value decomposition. During the calibration process the spatial orientation of the platform is not needed. Therefore no additional hardware is required. The functionality and accuracy of the algorithm is shown using simulated sensor readings.

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.

Collision avoidance is very important for autonomous sailing with many boats, e.g., during races. However, collision detection based on sensor data is complicated by the sails and boat motion. Particularly small boats cannot be equipped with sophisticated sensors, e.g., due to weight and power limitations. One approach to overcome this problem is to collect and store data from all participating vessels in a central data store. This World Server then provides the data to all boats, i.e., all participants in a race have access to a global view of the race situation. We present our basic server implementation and first test results indicating that the approach allows implementing and testing collision avoidance without the need for bulky and expensive sensors.