Search Results for author: Johan Vertens

Found 8 papers, 4 papers with code

Improving Deep Dynamics Models for Autonomous Vehicles with Multimodal Latent Mapping of Surfaces

no code implementations21 Mar 2023 Johan Vertens, Nicolai Dorka, Tim Welschehold, Michael Thompson, Wolfram Burgard

By training everything end-to-end with the loss of the dynamics model, we enforce the latent mapper to learn an update rule for the latent map that is useful for the subsequent dynamics model.

Autonomous Vehicles

USegScene: Unsupervised Learning of Depth, Optical Flow and Ego-Motion with Semantic Guidance and Coupled Networks

no code implementations15 Jul 2022 Johan Vertens, Wolfram Burgard

In this paper we propose USegScene, a framework for semantically guided unsupervised learning of depth, optical flow and ego-motion estimation for stereo camera images using convolutional neural networks.

Motion Estimation Optical Flow Estimation

Lane Graph Estimation for Scene Understanding in Urban Driving

1 code implementation1 May 2021 Jannik Zürn, Johan Vertens, Wolfram Burgard

Lane-level scene annotations provide invaluable data in autonomous vehicles for trajectory planning in complex environments such as urban areas and cities.

Autonomous Driving Lane Detection +2

HeatNet: Bridging the Day-Night Domain Gap in Semantic Segmentation with Thermal Images

no code implementations10 Mar 2020 Johan Vertens, Jannik Zürn, Wolfram Burgard

We avoid the expensive annotation of nighttime images by leveraging an existing daytime RGB-dataset and propose a teacher-student training approach that transfers the dataset's knowledge to the nighttime domain.

Autonomous Driving Camera Calibration +3

Learning Object Placements For Relational Instructions by Hallucinating Scene Representations

2 code implementations23 Jan 2020 Oier Mees, Alp Emek, Johan Vertens, Wolfram Burgard

One particular requirement for such robots is that they are able to understand spatial relations and can place objects in accordance with the spatial relations expressed by their user.

Auxiliary Learning Robotic Grasping +2

A Maximum Likelihood Approach to Extract Finite Planes from 3-D Laser Scans

1 code implementation23 Oct 2019 Alexander Schaefer, Johan Vertens, Daniel Büscher, Wolfram Burgard

Whether it is object detection, model reconstruction, laser odometry, or point cloud registration: Plane extraction is a vital component of many robotic systems.

Clustering object-detection +2

Long-Term Urban Vehicle Localization Using Pole Landmarks Extracted from 3-D Lidar Scans

1 code implementation23 Oct 2019 Alexander Schaefer, Daniel Büscher, Johan Vertens, Lukas Luft, Wolfram Burgard

Due to their ubiquity and long-term stability, pole-like objects are well suited to serve as landmarks for vehicle localization in urban environments.

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

no code implementations14 Sep 2017 Florian Kraemer, Alexander Schaefer, Andreas Eitel, Johan Vertens, Wolfram Burgard

Agricultural robots are expected to increase yields in a sustainable way and automate precision tasks, such as weeding and plant monitoring.


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