  This paper presents a novel indoor layout estimation system based on the
fusion of 2D LiDAR and intensity camera data. A ground robot explores an indoor
space with a single floor and vertical walls, and collects a sequence of
intensity images and 2D LiDAR datasets. The LiDAR provides accurate depth
information, while the camera captures high-resolution data for semantic
interpretation. The alignment of sensor outputs and image segmentation are
computed jointly by aligning LiDAR points, as samples of the room contour, to
ground-wall boundaries in the images. The alignment problem is decoupled into a
top-down view projection and a 2D similarity transformation estimation, which
can be solved according to the vertical vanishing point and motion of two
sensors. The recursive random sample consensus algorithm is implemented to
generate, evaluate and optimize multiple hypotheses with the sequential
measurements. The system allows jointly analyzing the geometric interpretation
from different sensors without offline calibration. The ambiguity in images for
ground-wall boundary extraction is removed with the assistance of LiDAR
observations, which improves the accuracy of semantic segmentation. The
localization and mapping is refined using the fused data, which enables the
system to work reliably in scenes with low texture or low geometric features.
