Zobrazeno 1 - 10
of 83
pro vyhledávání: '"Wenzel, Patrick"'
In this paper, we present a novel visual SLAM and long-term localization benchmark for autonomous driving in challenging conditions based on the large-scale 4Seasons dataset. The proposed benchmark provides drastic appearance variations caused by sea
Externí odkaz:
http://arxiv.org/abs/2301.01147
Autor:
Vogel, Arndt *, Saborowski, Anna, Wenzel, Patrick, Wege, Henning, Folprecht, Gunnar, Kretzschmar, Albrecht, Schütt, Philipp, Jacobasch, Lutz, Ziegenhagen, Nicolas, Boeck, Stefan, Zhang, Danmei, Kanzler, Stephan, Belle, Sebastian, Mohm, Johannes, Gökkurt, Eray, Lerchenmüller, Christian, Graeven, Ullrich, Pink, Daniel, Götze, Thorsten, Kirstein, Martha M
Publikováno v:
In The Lancet Gastroenterology & Hepatology August 2024 9(8):734-744
Vision-based learning methods for self-driving cars have primarily used supervised approaches that require a large number of labels for training. However, those labels are usually difficult and expensive to obtain. In this paper, we demonstrate how a
Externí odkaz:
http://arxiv.org/abs/2103.11204
Obstacle avoidance is a fundamental and challenging problem for autonomous navigation of mobile robots. In this paper, we consider the problem of obstacle avoidance in simple 3D environments where the robot has to solely rely on a single monocular ca
Externí odkaz:
http://arxiv.org/abs/2103.04727
We present LM-Reloc -- a novel approach for visual relocalization based on direct image alignment. In contrast to prior works that tackle the problem with a feature-based formulation, the proposed method does not rely on feature matching and RANSAC.
Externí odkaz:
http://arxiv.org/abs/2010.06323
Autor:
Wenzel, Patrick, Wang, Rui, Yang, Nan, Cheng, Qing, Khan, Qadeer, von Stumberg, Lukas, Zeller, Niclas, Cremers, Daniel
We present a novel dataset covering seasonal and challenging perceptual conditions for autonomous driving. Among others, it enables research on visual odometry, global place recognition, and map-based re-localization tracking. The data was collected
Externí odkaz:
http://arxiv.org/abs/2009.06364
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
The ability of deep learning models to generalize well across different scenarios depends primarily on the quality and quantity of annotated data. Labeling large amounts of data for all possible scenarios that a model may encounter would not be feasi
Externí odkaz:
http://arxiv.org/abs/1907.11025
Direct SLAM methods have shown exceptional performance on odometry tasks. However, they are susceptible to dynamic lighting and weather changes while also suffering from a bad initialization on large baselines. To overcome this, we propose GN-Net: a
Externí odkaz:
http://arxiv.org/abs/1904.11932
In this paper, we present a framework to control a self-driving car by fusing raw information from RGB images and depth maps. A deep neural network architecture is used for mapping the vision and depth information, respectively, to steering commands.
Externí odkaz:
http://arxiv.org/abs/1902.04272