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pro vyhledávání: '"Vertens, Johan"'
The safe deployment of autonomous vehicles relies on their ability to effectively react to environmental changes. This can require maneuvering on varying surfaces which is still a difficult problem, especially for slippery terrains. To address this i
Externí odkaz:
http://arxiv.org/abs/2303.11756
Autor:
Vertens, Johan, Burgard, Wolfram
In this paper we propose USegScene, a framework for semantically guided unsupervised learning of depth, optical flow and ego-motion estimation for stereo camera images using convolutional neural networks. Our framework leverages semantic information
Externí odkaz:
http://arxiv.org/abs/2207.07469
Lane-level scene annotations provide invaluable data in autonomous vehicles for trajectory planning in complex environments such as urban areas and cities. However, obtaining such data is time-consuming and expensive since lane annotations have to be
Externí odkaz:
http://arxiv.org/abs/2105.00195
The majority of learning-based semantic segmentation methods are optimized for daytime scenarios and favorable lighting conditions. Real-world driving scenarios, however, entail adverse environmental conditions such as nighttime illumination or glare
Externí odkaz:
http://arxiv.org/abs/2003.04645
Robots coexisting with humans in their environment and performing services for them need the ability to interact with them. One particular requirement for such robots is that they are able to understand spatial relations and can place objects in acco
Externí odkaz:
http://arxiv.org/abs/2001.08481
Publikováno v:
International Conference on Robotics and Automation , Montreal, QC, Canada, 2019, pp. 72-78
Whether it is object detection, model reconstruction, laser odometry, or point cloud registration: Plane extraction is a vital component of many robotic systems. In this paper, we propose a strictly probabilistic method to detect finite planes in org
Externí odkaz:
http://arxiv.org/abs/1910.11146
Publikováno v:
European Conference on Mobile Robots, Prague, Czech Republic, 2019, pp. 1-7
Due to their ubiquity and long-term stability, pole-like objects are well suited to serve as landmarks for vehicle localization in urban environments. In this work, we present a complete mapping and long-term localization system based on pole landmar
Externí odkaz:
http://arxiv.org/abs/1910.10550
Agricultural robots are expected to increase yields in a sustainable way and automate precision tasks, such as weeding and plant monitoring. At the same time, they move in a continuously changing, semi-structured field environment, in which features
Externí odkaz:
http://arxiv.org/abs/1709.04751
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