An Incrementally Deployed Swarm of MAVs for Localization UsingUltra-Wideband

Knowing the position of a moving target can be crucial, for example when localizing a first responder in an emergency scenario. In recent years, ultra wideband (UWB) has gained a lot of attention due to its localization accuracy. Unfortunately, UWB solutions often demand a manual setup in advance. This is tedious at best and not possible at all in environments with access restrictions (e.g., collapsed buildings). Thus, we propose a solution combining UWB with micro air vehicles (MAVs) to allow for UWB localization in a priori inaccessible environments. More precisely, MAVs equipped with UWB sensors are deployed incrementally into the environment. They localize themselves based on previously deployed MAVs and on-board odometry, before they land and enhance the UWB mesh network themselves. We tested this solution in a lab environment using a motion capture system for ground truth. Four MAVs were deployed as anchors and a fifth MAV was localized for over 80 second at a root mean square (RMS) of 0.206 m averaged over five experiments. For comparison, a setup with ideal anchor position knowledge came with 20 % lower RMS, and a setup purely based on odometry with 81 % higher RMS. The absolute scale of the error with the proposed approach is expected to be low enough for applications envisioned within the scope of this paper (e.g., the localization of a first responder) and thus considered a step towards flexible and accurate localization in a priori inaccessible, GNSS-denied environments.acceptedVersio

Quadrotor navigation in GPS-denied environments using multi-sensor data fusion

Zusammenfassung in deutscher SpracheAbweichender Titel nach Übersetzung der Verfasserin/des VerfassersThe use of unmanned aerial vehicles (UAVs) has increased tremendously over the last decades. With progressing computational power, smaller mechanical setups have evolved, which can enter constrained, GPS-denied environments. This work deals with the development of a state estimator for a quadrotor in such scenarios. On the chosen platform, the algorithms are implemented on an Atom-based board and are partially performed by an autopilot. An open-source flight stack is flashed on the autopilot, taking care of all low-level commands such as motor control. The board runs the Robot Operating System (ROS), which allows to modularly combine different modules. Sensor data from an inertial measurement unit (IMU), a laser-based distance sensor, and an optical flow sensor are available in ROS. Additionally, a Simultaneous Localization and Mapping (SLAM) algorithm determines pose estimates based on images of an RGB-D camera. The proposed state estimator, an extended Kalman filter (EKF) implemented as another C++ ROS node, makes use of this data. It is based on a kinematic model of the quadrotors pose and linear velocity, utilizing quaternions for the rotation representation. A multiplicative formulation of the EKF accounts for the quaternions. The autopilot generates the motor inputs based on the position and orientation setpoints. A further aim of this work is to compare the impact of two different methods for providing the setpoints. On the one hand, a C++ file outputs them one by one after reaching the previous goal. On the other hand, an external time-optimal trajectory planner outputs a number of intermediate points simultaneously. The EKF is tested both in simulation and on real hardware in an office environment. Its performance is worse compared to the one of the autopilots internal state estimator. Still, tracking is possible even during partial loss of measurements. Providing time-optimal trajectories improves the measurement acquisition and is hence the preferable method for delivering the flight commands7