@INPROCEEDINGS{2020:FabianHeightmapPosePrediction,
  author = {Stefan Fabian and Stefan Kohlbrecher and Oskar von Stryk},
  title = {Pose Prediction for Mobile Ground Robots in Uneven Terrain Based on Difference of Heightmaps},
  year = {2020},
  pages = {49-56},
  booktitle = {2020 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR)},
  doi = {10.1109/SSRR50563.2020.9292574},
  pdf = {2020_fabian_ssrr_pose_prediction_difference_heightmaps.pdf},
  abstract = {For traversing uneven terrain in degraded environments, determining the static stability and consequently the tip-over risk of a mobile ground rescue robot is fundamental for planning and evaluation of paths. This paper presents a novel iterative geometric method that reduces the problem of robot pose prediction to two-dimensional image-processing operations by introducing the concept of a robot heightmap. The presented method requires only geometrical and mass information extracted from the widely used unified robot description format (URDF) to compute the robot heightmap, which makes it transferable to a wide range of mobile robot platforms without modification. We demonstrate that the approach accurately predicts the real robot"s 6D pose at the input x-y-coordinates. Runtimes allowing the evaluation of poses in the order of ten thousand poses per second show that the method is computationally efficient enough to be used in online path planning.},
}

@MASTERSTHESIS{2019:FabianMAThesis,
  author = {Stefan Fabian},
  title = {Online 3D Path Planning and Exploration for Autonomous Mobile Robots in Unstructured Environments},
  year = {2019},
  school = {Technical University of Darmstadt, Computer Science Department},
  pdf = {2019_Fabian_MA.pdf},
  owner = {Stefan Fabian},
  abstract = {For the past decades, path planning for robots has been an active field of research. Recent advances in computation have enabled the introduction of additional constraints and incorporating terrain structure. In rescue robotics, in particular, the expected deployment areas are unknown and unstructured environments either in nature or due to structural changes, e.g., a partially collapsed building. To be able to operate in unstructured terrains, autonomous unmanned ground vehicles have to predict their orientation and the contact points with the ground in order to prevent situations in which the robot may tip-over and require human intervention. Current approaches demand significant computation time to estimate the robot"s pose and contact points, rendering them incapable of being used online on a robot without a connection to a remote operator station. This work presents a path planning approach using a pose prediction heuristic to quickly predict the robot"s pose consisting of its location and orientation, including the contact points with the ground. The proposed algorithm can plan stable paths in real-time on limited hardware resources. It is evaluated on a robot and in simulation. On the robot, the real-time planning capability and a parameterized trade-off between stability are demonstrated. In the simulation, the accuracy of the pose prediction is evaluated. Finally, the computation time is evaluated for different graph resolutions, and it is shown that the presented approach is capable of planning stable paths on limited resources in real-time.},
}

@BACHELORTHESIS{2016:FabianBAThesisPlanningUI,
  author = {Stefan Fabian},
  title = {Development of a User Interface and Back-End for Planning Tasks using Autonomous Robots},
  year = {2016},
  school = {Technical University of Darmstadt, Computer Science Department},
  pdf = {2016_Fabian_BA.pdf},
}