Zobrazeno 1 - 10
of 136
pro vyhledávání: '"Horn, Markus"'
Hand-eye calibration is an important and extensively researched method for calibrating rigidly coupled sensors, solely based on estimates of their motion. Due to the geometric structure of this problem, at least two motion estimates with non-parallel
Externí odkaz:
http://arxiv.org/abs/2308.06045
Publikováno v:
2023 IEEE Intelligent Vehicles Symposium (IV)
We propose a certifiably globally optimal approach for solving the hand-eye robot-world problem supporting multiple sensors and targets at once. Further, we leverage this formulation for estimating a geo-referenced calibration of infrastructure senso
Externí odkaz:
http://arxiv.org/abs/2305.01407
Publikováno v:
2021 International Conference on 3D Vision (3DV)
In this work, we present an approach for monocular hand-eye calibration from per-sensor ego-motion based on dual quaternions. Due to non-metrically scaled translations of monocular odometry, a scaling factor has to be estimated in addition to the rot
Externí odkaz:
http://arxiv.org/abs/2201.04473
Autor:
Griebel, Thomas, Authaler, Dominik, Horn, Markus, Henning, Matti, Buchholz, Michael, Dietmayer, Klaus
For autonomous driving, radar is an important sensor type. On the one hand, radar offers a direct measurement of the radial velocity of targets in the environment. On the other hand, in literature, radar sensors are known for their robustness against
Externí odkaz:
http://arxiv.org/abs/2109.09401
Publikováno v:
2018 Sensor Data Fusion: Trends, Solutions, Applications (SDF)
A complete overview of the surrounding vehicle environment is important for driver assistance systems and highly autonomous driving. Fusing results of multiple sensor types like camera, radar and lidar is crucial for increasing the robustness. The de
Externí odkaz:
http://arxiv.org/abs/2103.02278
Publikováno v:
IEEE Robotics and Automation Letters 6 (2), 982-989, 2021
In this work, we propose an approach for extrinsic sensor calibration from per-sensor ego-motion estimates. Our problem formulation is based on dual quaternions, enabling two different online capable solving approaches. We provide a certifiable globa
Externí odkaz:
http://arxiv.org/abs/2101.11440
Publikováno v:
2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)
This work addresses the problem of point cloud registration using deep neural networks. We propose an approach to predict the alignment between two point clouds with overlapping data content, but displaced origins. Such point clouds originate, for ex
Externí odkaz:
http://arxiv.org/abs/2007.11255
We address the problem of vehicle self-localization from multi-modal sensor information and a reference map. The map is generated off-line by extracting landmarks from the vehicle's field of view, while the measurements are collected similarly on the
Externí odkaz:
http://arxiv.org/abs/1904.09007
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.