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Found 11 papers, 2 papers with code
4Seasons: Benchmarking Visual SLAM and Long-Term Localization for Autonomous Driving in Challenging Conditions
In this paper, we present a novel visual SLAM and long-term localization benchmark for autonomous driving in challenging conditions based on the large-scale 4Seasons dataset.
Self-Supervised Steering Angle Prediction for Vehicle Control Using Visual Odometry
Vision-based learning methods for self-driving cars have primarily used supervised approaches that require a large number of labels for training.
Vision-Based Mobile Robotics Obstacle Avoidance With Deep Reinforcement Learning
Obstacle avoidance is a fundamental and challenging problem for autonomous navigation of mobile robots.
LM-Reloc: Levenberg-Marquardt Based Direct Visual Relocalization
The learned features significantly improve the robustness of direct image alignment, especially for relocalization across different conditions.
4Seasons: A Cross-Season Dataset for Multi-Weather SLAM in Autonomous Driving
We present a novel dataset covering seasonal and challenging perceptual conditions for autonomous driving.
Towards Generalizing Sensorimotor Control Across Weather Conditions
The ability of deep learning models to generalize well across different scenarios depends primarily on the quality and quantity of annotated data.
GN-Net: The Gauss-Newton Loss for Multi-Weather Relocalization
Direct SLAM methods have shown exceptional performance on odometry tasks.
Towards Self-Supervised High Level Sensor Fusion
In this paper, we present a framework to control a self-driving car by fusing raw information from RGB images and depth maps.
Semantic Label Reduction Techniques for Autonomous Driving
Semantic segmentation maps can be used as input to models for maneuvering the controls of a car.
Latent Space Reinforcement Learning for Steering Angle Prediction
The control module trained with reinforcement learning takes the latent vector as input to predict the correct steering angle.
Modular Vehicle Control for Transferring Semantic Information Between Weather Conditions Using GANs
To this end, we propose to divide the task of vehicle control into two independent modules: a control module which is only trained on one weather condition for which labeled steering data is available, and a perception module which is used as an interface between new weather conditions and the fixed control module.
