Abstract. In this paper, we present SURE features -a novel combination of interest point detector and descriptor for 3D point clouds and depth images. We propose an entropy-based interest operator that selects distinctive points on surfaces. It measures the variation in surface orientation from surface normals in the local vicinity of a point. We complement our approach by the design of a view-pose-invariant descriptor that captures local surface curvature properties, and we propose optional means to incorporate colorful texture information seamlessly. In experiments, we compare our approach to a state-of-the-art feature detector in depth images (NARF) and demonstrate similar repeatability of our detector. Our novel pair of detector and descriptor achieves superior results for matching interest points between images and also requires lower computation time.

Due to the advancements of robotic systems, they are able to be employed in more unstructured outdoor environments. In such environments the robot-terrain interaction becomes a highly non-linear function. Several methods were proposed to estimate the robot-terrain interaction: machine learning methods, iterative geometric methods, quasi-static and fully dynamic physics simulations. However, to the best of our knowledge there has been no systematic evaluation comparing those methods. In this paper, we present a newly developed iterative contact point estimation method for static stability estimation of actively reconfigurable robots. This new method is systematically compared to a physics simulation in a comprehensive evaluation. Both interaction models determine the contact points between robot and terrain and facilitate a subsequent static stability prediction. Hence, they can be used in our state space global planner for rough terrain to evaluate the robot's pose and stability. The analysis also compares deterministic versions of both methods to stochastic versions which account for uncertainty in the robot configuration and the terrain model. The results of this analysis show that the new iterative method is a valid and fast approximate method. It is significantly faster compared to a physics simulation while providing good results in realistic robotic scenarios

Abstract-In this paper, we present a variant of SURE, an interest point detector and descriptor for 3D point clouds and depth images and use it for recognizing semantically distinct places in indoor environments. The SURE interest operator selects distinctive points on surfaces by measuring the variation in surface orientation based on surface normals in the local vicinity of a point. Furthermore SURE includes a view-poseinvariant descriptor that captures local surface properties and incorporates colored texture information. In experiments, we compare our approach to a state-of-the-art feature detector in depth images (NARF). Finally, we evaluate the use of SURE features for recognizing places and demonstrate its advantages.

ABSTRACT:3D terrain models are an important instrument in areas like geology, agriculture and reconnaissance. Using an automated UAS with a line-based LiDAR can create terrain models fast and easily even from large areas. But the resulting point cloud may contain holes and therefore be incomplete. This might happen due to occlusions, a missed flight route due to wind or simply as a result of changes in the ground height which would alter the swath of the LiDAR system. This paper proposes a method to detect holes in 3D point clouds generated during the flight and adjust the course in order to close them. First, a grid-based search for holes in the horizontal ground plane is performed. Then a check for vertical holes mainly created by buildings walls is done. Due to occlusions and steep LiDAR angles, closing the vertical gaps may be difficult or even impossible. Therefore, the current approach deals with holes in the ground plane and only marks the vertical holes in such a way that the operator can decide on further actions regarding them. The aim is to efficiently create point clouds which can be used for the generation of complete 3D terrain models.

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