Alexander Schaefer

Orcid: 0000-0002-5207-8403

Affiliations:
  • University of Freiburg, Germany (PhD 2020)


According to our database1, Alexander Schaefer authored at least 14 papers between 2017 and 2021.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

Online presence:

On csauthors.net:

Bibliography

2021
Long-term vehicle localization in urban environments based on pole landmarks extracted from 3-D lidar scans.
Robotics Auton. Syst., 2021

Building an Aerial-Ground Robotics System for Precision Farming: An Adaptable Solution.
IEEE Robotics Autom. Mag., 2021

2020
Highly accurate lidar-based mapping and localization for mobile robots
PhD thesis, 2020

2019
On the Bayes Filter for Shared Autonomy.
IEEE Robotics Autom. Lett., 2019

Building an Aerial-Ground Robotics System for Precision Farming.
CoRR, 2019

A Maximum Likelihood Approach to Extract Finite Planes from 3-D Laser Scans.
Proceedings of the International Conference on Robotics and Automation, 2019

Long-Term Urban Vehicle Localization Using Pole Landmarks Extracted from 3-D Lidar Scans.
Proceedings of the 2019 European Conference on Mobile Robots, 2019

2018
DCT Maps: Compact Differentiable Lidar Maps Based on the Cosine Transform.
IEEE Robotics Autom. Lett., 2018

Detecting Changes in the Environment Based on Full Posterior Distributions Over Real-Valued Grid Maps.
IEEE Robotics Autom. Lett., 2018

A Maximum Likelihood Approach to Extract Polylines from 2-D Laser Range Scans.
Proceedings of the 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2018

2017
An Analytical Lidar Sensor Model Based on Ray Path Information.
IEEE Robotics Autom. Lett., 2017

Agricultural robot dataset for plant classification, localization and mapping on sugar beet fields.
Int. J. Robotics Res., 2017

From Plants to Landmarks: Time-invariant Plant Localization that uses Deep Pose Regression in Agricultural Fields.
CoRR, 2017

Closed-form full map posteriors for robot localization with lidar sensors.
Proceedings of the 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2017


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