7 citations · 14 across the 5 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
Semantic Label Reduction Techniques for Autonomous Driving
Qadeer Khan, Torsten Schön, Patrick Wenzel
Semantic segmentation maps can be used as input to models for maneuvering the controls of a car. However, not all labels may be necessary for making the control decision. One would…
Latent Space Reinforcement Learning for Steering Angle Prediction
Qadeer Khan, Torsten Schön, Patrick Wenzel
Model-free reinforcement learning has recently been shown to successfully learn navigation policies from raw sensor data. In this work, we address the problem of learning driving p…