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Daniel Althoff
Researcher at Technische Universität München
Publications - 21
Citations - 789
Daniel Althoff is an academic researcher from Technische Universität München. The author has contributed to research in topics: Collision & Motion planning. The author has an hindex of 10, co-authored 16 publications receiving 708 citations.
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Proceedings ArticleDOI
Comparison of surface normal estimation methods for range sensing applications
TL;DR: A detailed analysis and comparison of existing methods for surface normal estimation with a special emphasis on the trade-off between quality and speed is presented and guidelines on choosing the ‘right’ algorithm for the robotics practitioner are provided.
Proceedings ArticleDOI
Interactive scene prediction for automotive applications
Andreas Lawitzky,Daniel Althoff,Christoph F. Passenberg,Georg Tanzmeister,Dirk Wollherr,Martin Buss +5 more
TL;DR: A framework for motion prediction of vehicles and safety assessment of traffic scenes is presented, which is inspired by an optimization problem and can be used for driver assistant systems as well as for autonomous driving applications.
Journal ArticleDOI
Safety assessment of robot trajectories for navigation in uncertain and dynamic environments
TL;DR: In this article, the authors present a probabilistic framework for reasoning about the safety of robot trajectories in dynamic and uncertain environments with imperfect information about the future motion of surrounding objects.
Proceedings ArticleDOI
Safety verification of autonomous vehicles for coordinated evasive maneuvers
TL;DR: The possible set of deviations is computed with methods from reachability analysis, which allows to verify evasive maneuvers under consideration of the mentioned uncertainties, and has a short response time, which can be applied for real time safety decisions.
Proceedings ArticleDOI
Probabilistic collision state checker for crowded environments
TL;DR: This work extends the concept of ICS to probabilistic collision states (PCS), which estimates the collision probability for a given state, and shows a significant difference in interaction behavior for active and non-deterministic moving obstacles in the robot workspace.