N
Niclas Vödisch
Researcher at Institute of Robotics and Intelligent Systems
Publications - 7
Citations - 28
Niclas Vödisch is an academic researcher from Institute of Robotics and Intelligent Systems. The author has contributed to research in topics: Computer science & Benchmark (computing). The author has an hindex of 2, co-authored 5 publications receiving 10 citations.
Papers
More filters
Proceedings ArticleDOI
Accurate Mapping and Planning for Autonomous Racing
Leiv Andresen,Adrian Brandemuehl,Alex Hönger,Benson Kuan,Niclas Vödisch,Hermann Blum,Victor Reijgwart,Lukas Bernreiter,Lukas Schaupp,Jen Jen Chung,Mathias Bürki,Martin R. Oswald,Roland Siegwart,Abel Gawel +13 more
TL;DR: In this article, the authors present the perception, mapping, and planning pipeline implemented on an autonomous race car for the 2019 Formula Student Germany (FSG) 2019 driverless competition.
Proceedings ArticleDOI
Accurate Mapping and Planning for Autonomous Racing
Leiv Andresen,Adrian Brandemuehl,Alex Hönger,Benson Kuan,Niclas Vödisch,Hermann Blum,Victor Reijgwart,Lukas Bernreiter,Lukas Schaupp,Jen Jen Chung,Mathias Bürki,Martin R. Oswald,Roland Siegwart,Abel Gawel +13 more
TL;DR: The presented solution combines early fusion of camera and LiDAR data, a layered mapping approach, and a planning approach that uses Bayesian filtering to achieve high-speed driving on unknown race tracks while creating accurate maps.
Posted Content
FSOCO: The Formula Student Objects in Context Dataset.
TL;DR: The FSOCO dataset is presented, a collaborative dataset for vision-based cone detection systems in Formula Student Driverless competitions that contains human annotated ground truth labels for both bounding boxes and instance-wise segmentation masks.
Posted Content
Fast and Accurate Mapping for Autonomous Racing.
Leiv Andresen,Adrian Brandemuehl,Alex Hönger,Benson Kuan,Niclas Vödisch,Hermann Blum,Victor Reijgwart,Lukas Bernreiter,Lukas Schaupp,Jen Jen Chung,Mathias Bürki,Martin R. Oswald,Roland Siegwart,Abel Gawel +13 more
TL;DR: The presented solution combines early fusion of camera and LiDAR data, a layered mapping approach, and a planning approach that uses Bayesian filtering to achieve high-speed driving on unknown race tracks while creating accurate maps.
Journal ArticleDOI
CoVIO: Online Continual Learning for Visual-Inertial Odometry
TL;DR: CoVIO as mentioned in this paper proposes a sampling strategy to maximize image diversity in a fixed-size replay buffer that targets the limited storage capacity of embedded devices, and decouples the odometry estimation from the network weight update step enabling continuous inference in real time.