@article{StrohmeierWalterRotheetal.2018,
  author    = {Strohmeier, Michael and Walter, Thomas and Rothe, Julian and Montenegro, Sergio},
  title     = {Ultra-wideband based pose estimation for small unmanned aerial vehicles},
  series = {IEEE Access},
  volume    = {6},
  journal   = {IEEE Access},
  doi       = {10.1109/ACCESS.2018.2873571},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-177503},
  pages     = {57526-57535},
  year      = {2018},
  abstract  = {This paper proposes a 3-D local pose estimation system for a small Unmanned Aerial Vehicle (UAV) with a weight limit of 200 g and a very small footprint of 10 cm×10cm. The system is realized by fusing 3-D position estimations from an Ultra-Wide Band (UWB) transceiver network with Inertial Measurement Unit (IMU) sensor data and data from a barometric pressure sensor. The 3-D position from the UWB network is estimated using Multi-Dimensional Scaling (MDS) and range measurements between the transceivers. The range measurements are obtained using Double-Sided Two-Way Ranging (DS-TWR), thus eliminating the need for an additional clock synchronization mechanism. The sensor fusion is accomplished using a loosely coupled Extended Kalman Filter (EKF) architecture. Extensive evaluation of the proposed system shows that a position accuracy with a Root-Mean-Square Error (RMSE) of 0.20cm can be obtained. The orientation angle can be estimated with an RMSE of 1.93°.},
  language  = {en}
}
@article{GageikBenzMontenegro2015,
  author    = {Gageik, Nils and Benz, Paul and Montenegro, Sergio},
  title     = {Obstacle Detection and Collision Avoidance for a UAV with Complementary Low-Cost Sensors},
  series = {IEEE Access},
  volume    = {3},
  journal   = {IEEE Access},
  doi       = {10.1109/ACCESS.2015.2432455},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-125481},
  pages     = {599 - 609},
  year      = {2015},
  abstract  = {This paper demonstrates an innovative and simple solution for obstacle detection and collision avoidance of unmanned aerial vehicles (UAVs) optimized for and evaluated with quadrotors. The sensors exploited in this paper are low-cost ultrasonic and infrared range finders, which are much cheaper though noisier than more expensive sensors such as laser scanners. This needs to be taken into consideration for the design, implementation, and parametrization of the signal processing and control algorithm for such a system, which is the topic of this paper. For improved data fusion, inertial and optical flow sensors are used as a distance derivative for reference. As a result, a UAV is capable of distance controlled collision avoidance, which is more complex and powerful than comparable simple solutions. At the same time, the solution remains simple with a low computational burden. Thus, memory and time-consuming simultaneous localization and mapping is not required for collision avoidance.},
  language  = {en}
}