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

Indoor Layout Estimation by 2D LiDAR and Camera Fusion