activity
20182022
most citedTeam NimbRo at MBZIRC 2017: Fast Landing on a Moving Target and Treasure Hunting with a Team of MAVs

35 citations · 79 across the 6 of their papers we have counts for

collaborators
Showing cs.CVShow all

7 papers · 1 filter

cs.CV202230 cited

Real-Time Multi-Modal Semantic Fusion on Unmanned Aerial Vehicles with Label Propagation for Cross-Domain Adaptation

Simon Bultmann, Jan Quenzel, Sven Behnke

Unmanned aerial vehicles (UAVs) equipped with multiple complementary sensors have tremendous potential for fast autonomous or remote-controlled semantic scene analysis, e.g., for d…

cs.CV2021

Real-Time Multi-Modal Semantic Fusion on Unmanned Aerial Vehicles

Simon Bultmann, Jan Quenzel, Sven Behnke

Unmanned aerial vehicles (UAVs) equipped with multiple complementary sensors have tremendous potential for fast autonomous or remote-controlled semantic scene analysis, e.g., for d…

cs.CV2021

LatticeNet: Fast Spatio-Temporal Point Cloud Segmentation Using Permutohedral Lattices

Radu Alexandru Rosu, Peer Schütt, Jan Quenzel +1

Deep convolutional neural networks (CNNs) have shown outstanding performance in the task of semantically segmenting images. Applying the same methods on 3D data still poses challen…

cs.CV2020

Beyond Photometric Consistency: Gradient-based Dissimilarity for Improving Visual Odometry and Stereo Matching

Jan Quenzel, Radu Alexandru Rosu, Thomas Läbe +2

Pose estimation and map building are central ingredients of autonomous robots and typically rely on the registration of sensor data. In this paper, we investigate a new metric for…

cs.CV2019

LatticeNet: Fast Point Cloud Segmentation Using Permutohedral Lattices

Radu Alexandru Rosu, Peer Schütt, Jan Quenzel +1

Deep convolutional neural networks (CNNs) have shown outstanding performance in the task of semantically segmenting images. However, applying the same methods on 3D data still pose…

cs.CV201914 cited

Semi-Supervised Semantic Mapping through Label Propagation with Semantic Texture Meshes

Radu Alexandru Rosu, Jan Quenzel, Sven Behnke

Scene understanding is an important capability for robots acting in unstructured environments. While most SLAM approaches provide a geometrical representation of the scene, a seman…