  This paper presents an algorithm for indoor layout estimation and
reconstruction through the fusion of a sequence of captured images and LiDAR
data sets. In the proposed system, a movable platform collects both intensity
images and 2D LiDAR information. Pose estimation and semantic segmentation is
computed jointly by aligning the LiDAR points to line segments from the images.
For indoor scenes with walls orthogonal to floor, the alignment problem is
decoupled into top-down view projection and a 2D similarity transformation
estimation and solved by the recursive random sample consensus (R-RANSAC)
algorithm. Hypotheses can be generated, evaluated and optimized by integrating
new scans as the platform moves throughout the environment. The proposed method
avoids the need of extensive prior training or a cuboid layout assumption,
which is more effective and practical compared to most previous indoor layout
estimation methods. Multi-sensor fusion allows the capability of providing
accurate depth estimation and high resolution visual information.
