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Computer Vision Group
TUM School of Computation, Information and Technology
Technical University of Munich

Technical University of Munich



Contact: Vladyslav Usenko.


In this paper, we present a real-time approach to local trajectory replanning for microaerial vehicles (MAVs). Current trajectory generation methods for multicopters achieve high success rates in cluttered environments, but assume that the environment is static and require prior knowledge of the map. In the presented study, we use the results of such planners and extend them with a local replanning algorithm that can handle unmodeled (possibly dynamic) obstacles while keeping the MAV close to the global trajectory. To ensure that the proposed approach is real-time capable, we maintain information about the environment around the MAV in an occupancy grid stored in a three-dimensional circular buffer, which moves together with a drone, and represent the trajectories by using uniform B-splines. This representation ensures that the trajectory is sufficiently smooth and simultaneously allows for efficient optimization.

Open-Source Code

The full source code is available on Github under LGPLv3 https://github.com/vsu91/ewok.

Some examples use the forest_gen dataset available here https://github.com/ethz-asl/forest_gen.


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Conference and Workshop Papers
[]Real-Time Trajectory Replanning for MAVs using Uniform B-splines and a 3D Circular Buffer (V. Usenko, L. von Stumberg, A. Pangercic and D. Cremers), In International Conference on Intelligent Robots and Systems (IROS), 2017. ([arxiv]) [bibtex] [pdf]Best Paper Award - Finalist (link)
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Computer Vision Group

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CVPR 2023

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NeurIPS 2022

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WACV 2023

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