Indoor Layout Estimation by 2D LiDAR and Camera Fusion
This addresses indoor mapping for robotics or AR applications, but it is incremental as it builds on existing fusion techniques with a specific alignment approach.
The paper tackles indoor layout estimation by fusing 2D LiDAR and camera data to reconstruct environments without extensive training or cuboid assumptions, achieving effective and practical results compared to prior methods.
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.