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of 13
pro vyhledávání: '"Frank Moosmann"'
Autor:
Stefan Andreas Baur, David Josef Emmerichs, Frank Moosmann, Peter Pinggera, Bjorn Ommer, Andreas Geiger
Publikováno v:
2021 IEEE/CVF International Conference on Computer Vision (ICCV).
Autor:
Christoph Stiller, Holger H. Rapp, Frank Moosmann, Benjamin Ranft, Julius Ziegler, Andreas Geiger, Martin Lauer
Publikováno v:
IEEE Transactions on Intelligent Transportation Systems. 13:1008-1017
In this paper, we present the concepts and methods developed for the autonomous vehicle known as AnnieWAY, which is our winning entry to the 2011 Grand Cooperative Driving Challenge. We describe algorithms for sensor fusion, vehicle-to-vehicle commun
Autor:
Frank Moosmann, Christoph Stiller
Publikováno v:
ICRA
Both, the estimation of the trajectory of a sensor and the detection and tracking of moving objects are essential tasks for autonomous robots. This work proposes a new algorithm that treats both problems jointly. The sole input is a sequence of dense
Publikováno v:
ICRA
As a core robotic and vision problem, camera and range sensor calibration have been researched intensely over the last decades. However, robotic research efforts still often get heavily delayed by the requirement of setting up a calibrated system con
Autor:
Frank Moosmann, Miro Sauerland
Publikováno v:
ICCV Workshops
Designing object models for a robot's detection-system can be very time-consuming since many object classes exist. This paper presents an approach that automatically infers object classes from recorded 3D data and collects training examples. A specia
Autor:
Frank Moosmann, Christoph Stiller
Publikováno v:
2011 IEEE Intelligent Vehicles Symposium (IV).
Publikováno v:
IROS
Visually estimating a robot's own motion has been an active field of research within the last years. Though impressive results have been reported, some application areas still exhibit huge challenges. Especially for car-like robots in urban environme
Autor:
Frank Moosmann, Thierry Fraichard
Publikováno v:
ICRA
Object-class independent motion estimation from range data is a challenging task. We present here a novel approach that is able to derive a dense motion field based on range images only. We propose to first segment the range image into segments using
Publikováno v:
2009 IEEE Intelligent Vehicles Symposium.
Present object detection methods working on 3D range data are so far either optimized for unstructured offroad environments or flat urban environments. We present a fast algorithm able to deal with tremendous amounts of 3D Lidar measurements. It uses
Publikováno v:
2009 IEEE Intelligent Vehicles Symposium.
This paper introduces a novel method for vehicle pose estimation and motion tracking using visual features. The method combines ideas from research on visual odometry with a feature map that is automatically generated from aerial images into a Visual