6 accepted papers
Estimating 3D occupancy and motion at the vehicle's surroundings is essential for autonomous driving, enabling situational awareness in dynamic environments. Existing approaches jointly learn geometry and motion but rely on expensive 3D occupancy and flow annotations, velocity labels from bounding b
LiDAR Semantic Segmentation is a fundamental task in autonomous driving perception consisting of associating each LiDAR point to a semantic label. Fully-supervised models have widely tackled this task, but they require labels for each scan, which either limits their domain or requires impractical am
Autonomous vehicles demand detailed maps to maneuver reliably through traffic, which need to be kept up-to-date to ensure a safe operation. A promising way to adapt the maps to the ever-changing road-network is to use crowd-sourced data from a fleet of vehicles. In this work, we present a mapping sy…
Lightweight and semantically meaningful environment maps are crucial for many applications in robotics and autonomous driving to facilitate higher-level tasks such as navigation and planning. In this paper we present a novel approach to incrementally build a meaningful and lightweight semantic map d…
Accurate and reliable localization is a fundamental requirement for autonomous vehicles to use map information in higher-level tasks such as navigation or planning. In this paper, we present a novel approach to vehicle localization in dense semantic maps, including vectorized high-definition maps or…
Highly accurate maps of the road infrastructure are a crucial cornerstone for self-driving cars to enable navigation in complex traffic scenarios. Traditional methods for creating detailed maps of road environments involve expensive survey vehicles that cannot keep up with the frequent changes in th…