Our Research
We investigate robots that can understand their environment semantically and geometrically, in order to perform manipulation and other safety critical tasks in proximity to humans. This encompasses semantic understanding under open-set conditions, map representations of the environment, active perception and planning, as well as adaptation and continual self-supervised learning.SNI-SLAM++: Tightly-Coupled Semantic Neural Implicit SLAM
S Zhu, G Wang, H Blum, Z Wang, G Zhang, D Cremers, M Pollefeys, H Wang
T-PAMI 2026
doiosmAG-LLM: Zero-Shot Open-Vocabulary Object Navigation via Semantic Maps and Large Language Models Reasoning
F Xie, S Schwertfeger, H Blum
IEEE Robotics and Automation Letters 2026
doiCroCoDL: Cross-device Collaborative Dataset for Localization
H Blum, A Mercurio, J O’Reilly, T Engelbracht, M Dusmanu, M Pollefeys, Z Bauer
CVPR 2025
doilocalizoo.comARKit LabelMaker: A New Scale for Indoor 3D Scene Understanding
G Ji, S Weder, F Engelmann, M Pollefeys, H Blum
CVPR 2025
arxivPDFlabelmaker.orgcodeDepthSplat: Connecting Gaussian Splatting and Depth
H Xu, S Peng, F Wang, H Blum, D Barath, A Geiger, M Pollefeys
CVPR 2025
arxivPDFwebsitecodeopenaccess.thecvf.comNeuSurfEmb: A Complete Pipeline for Dense Correspondence-based 6D Object Pose Estimation without CAD Models
F Milano, JJ Chung, H Blum, R Siegwart, L Ott
IROS 2024
SNI-SLAM: Semantic Neural Implicit SLAM
S Zhu, G Wang, H Blum, J Liu, L Song, M Pollefeys, H Wang
CVPR 2024
arxivPDFcodeopenaccess.thecvf.comLabelMaker: Automatic Semantic Label Generation from RGB-D Trajectories
S Weder, H Blum, F Engelmann, M Pollefeys
3DV 2024
arxivPDFlabelmaker.orgcodeRobot Perception and Learning Lab
