activity
20182021
most citedLatent Space Reinforcement Learning for Steering Angle Prediction

7 citations · 14 across the 5 of their papers we have counts for

collaborators

9 papers

cs.CV20212 cited

Self-Supervised Steering Angle Prediction for Vehicle Control Using Visual Odometry

Qadeer Khan, Patrick Wenzel, Daniel Cremers

Vision-based learning methods for self-driving cars have primarily used supervised approaches that require a large number of labels for training. However, those labels are usually…

cs.LG2021

Vision-Based Mobile Robotics Obstacle Avoidance With Deep Reinforcement Learning

Patrick Wenzel, Torsten Schön, Laura Leal-Taixé +1

Obstacle avoidance is a fundamental and challenging problem for autonomous navigation of mobile robots. In this paper, we consider the problem of obstacle avoidance in simple 3D en…

cs.CV2020

LM-Reloc: Levenberg-Marquardt Based Direct Visual Relocalization

Lukas von Stumberg, Patrick Wenzel, Nan Yang +1

We present LM-Reloc -- a novel approach for visual relocalization based on direct image alignment. In contrast to prior works that tackle the problem with a feature-based formulati…

cs.LG2019

Towards Generalizing Sensorimotor Control Across Weather Conditions

Qadeer Khan, Patrick Wenzel, Daniel Cremers +1

The ability of deep learning models to generalize well across different scenarios depends primarily on the quality and quantity of annotated data. Labeling large amounts of data fo…

cs.CV2019

GN-Net: The Gauss-Newton Loss for Multi-Weather Relocalization

Lukas von Stumberg, Patrick Wenzel, Qadeer Khan +1

Direct SLAM methods have shown exceptional performance on odometry tasks. However, they are susceptible to dynamic lighting and weather changes while also suffering from a bad init…

cs.LG20192 cited

Towards Self-Supervised High Level Sensor Fusion

Qadeer Khan, Torsten Schön, Patrick Wenzel

In this paper, we present a framework to control a self-driving car by fusing raw information from RGB images and depth maps. A deep neural network architecture is used for mapping…