36 citations · 87 across the 7 of their papers we have counts for
8 papers
User Feedback and Sample Weighting for Ill-Conditioned Hand-Eye Calibration
Markus Horn, Thomas Wodtko, Michael Buchholz +1
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 stru…
Extrinsic Infrastructure Calibration Using the Hand-Eye Robot-World Formulation
Markus Horn, Thomas Wodtko, Michael Buchholz +1
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 formulati…
Globally Optimal Multi-Scale Monocular Hand-Eye Calibration Using Dual Quaternions
Thomas Wodtko, Markus Horn, Michael Buchholz +1
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…
Anomaly Detection in Radar Data Using PointNets
Thomas Griebel, Dominik Authaler, Markus Horn +3
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 han…
Motion Classification and Height Estimation of Pedestrians Using Sparse Radar Data
Markus Horn, Ole Schumann, Markus Hahn +2
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 ca…
Online Extrinsic Calibration based on Per-Sensor Ego-Motion Using Dual Quaternions
Markus Horn, Thomas Wodtko, Michael Buchholz +1
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 diffe…
DeepCLR: Correspondence-Less Architecture for Deep End-to-End Point Cloud Registration
Markus Horn, Nico Engel, Vasileios Belagiannis +2
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 da…
DeepLocalization: Landmark-based Self-Localization with Deep Neural Networks
Nico Engel, Stefan Hoermann, Markus Horn +2
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'…