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Found 9 papers, 4 papers with code
A Smart Robotic System for Industrial Plant Supervision
In today's chemical plants, human field operators perform frequent integrity checks to guarantee high safety standards, and thus are possibly the first to encounter dangerous operating conditions.
Uncertainty-aware LiDAR Panoptic Segmentation
Current learning-based methods typically try to achieve maximum performance for this task, while neglecting a proper estimation of the associated uncertainties.
Uncertainty-aware Panoptic Segmentation
In this work, we introduce the novel task of uncertainty-aware panoptic segmentation, which aims to predict per-pixel semantic and instance segmentations, together with per-pixel uncertainty estimates.
Robust Monocular Localization in Sparse HD Maps Leveraging Multi-Task Uncertainty Estimation
Robust localization in dense urban scenarios using a low-cost sensor setup and sparse HD maps is highly relevant for the current advances in autonomous driving, but remains a challenging topic in research.
Courteous Behavior of Automated Vehicles at Unsignalized Intersections via Reinforcement Learning
Our reinforcement learning agent learns a policy for a centralized controller to let connected autonomous vehicles at unsignalized intersections give up their right of way and yield to other vehicles to optimize traffic flow.
EfficientLPS: Efficient LiDAR Panoptic Segmentation
Panoptic segmentation of point clouds is a crucial task that enables autonomous vehicles to comprehend their vicinity using their highly accurate and reliable LiDAR sensors.
Efficiency and Equity are Both Essential: A Generalized Traffic Signal Controller with Deep Reinforcement Learning
In this paper we present an approach to learning policies for signal controllers using deep reinforcement learning aiming for optimized traffic flow.
A Maximum Likelihood Approach to Extract Finite Planes from 3-D Laser Scans
Whether it is object detection, model reconstruction, laser odometry, or point cloud registration: Plane extraction is a vital component of many robotic systems.
Long-Term Urban Vehicle Localization Using Pole Landmarks Extracted from 3-D Lidar Scans
Due to their ubiquity and long-term stability, pole-like objects are well suited to serve as landmarks for vehicle localization in urban environments.
