Abstract
This paper presents a novel algorithm that utilizes a 2D
floorplan to align panorama RGBD scans. While effective
panorama RGBD alignment techniques exist, such a system requires extremely dense RGBD image sampling. Our
approach can significantly reduce the number of necessary
scans with the aid of a floorplan image. We formulate a
novel Markov Random Field inference problem as a scan
placement over the floorplan, as opposed to the conventional scan-to-scan alignment. The technical contributions
lie in multi-modal image correspondence cues (between
scans and schematic floorplan) as well as a novel coverage
potential avoiding an inherent stacking bias. The proposed
approach has been evaluated on five challenging large indoor spaces. To the best of our knowledge, we present the
first effective system that utilizes a 2D floorplan image for
building-scale 3D pointcloud alignment. The source code
and the data are shared with the community to further enhance indoor mapping research.