PanopticNDT: Efficient and Robust Panoptic Mapping
Daniel Seichter Benedict Stephan Söhnke Benedikt Fischedick Steffen Müller Leonard Rabes Horst-Michael Gross
As the application scenarios of mobile robots are getting more complex and
challenging, scene understanding becomes increasingly crucial. A mobile robot
that is supposed to operate autonomously in indoor environments must have
precise knowledge about what objects are present, where they are, what their
spatial extent is, and how they can be reached; i.e., information about free
space is also crucial. Panoptic mapping is a powerful instrument providing such
information. However, building 3D panoptic maps with high spatial resolution is
challenging on mobile robots, given their limited computing capabilities. In
this paper, we propose PanopticNDT - an efficient and robust panoptic mapping
approach based on occupancy normal distribution transform (NDT) mapping. We
evaluate our approach on the publicly available datasets Hypersim and
ScanNetV2. The results reveal that our approach can represent panoptic
information at a higher level of detail than other state-of-the-art approaches
while enabling real-time panoptic mapping on mobile robots. Finally, we prove
the real-world applicability of PanopticNDT with qualitative results in a
domestic application.