Furniture Free Mapping using 3D Lidars
This addresses the need for clutter-free maps for applications like room segmentation and long-term localization in robotics, representing an incremental improvement over existing point cloud-based methods.
The paper tackles the problem of generating furniture-free maps for indoor mobile robots by proposing a SLAM-based method using an orthogonal pair of Lidars, achieving 99.60% precision in retaining walls while removing furniture.
Mobile robots depend on maps for localization, planning, and other applications. In indoor scenarios, there is often lots of clutter present, such as chairs, tables, other furniture, or plants. While mapping this clutter is important for certain applications, for example navigation, maps that represent just the immobile parts of the environment, i.e. walls, are needed for other applications, like room segmentation or long-term localization. In literature, approaches can be found that use a complete point cloud to remove the furniture in the room and generate a furniture free map. In contrast, we propose a Simultaneous Localization And Mapping (SLAM)-based mobile laser scanning solution. The robot uses an orthogonal pair of Lidars. The horizontal scanner aims to estimate the robot position, whereas the vertical scanner generates the furniture free map. There are three steps in our method: point cloud rearrangement, wall plane detection and semantic labeling. In the experiment, we evaluate the efficiency of removing furniture in a typical indoor environment. We get $99.60\%$ precision in keeping the wall in the 3D result, which shows that our algorithm can remove most of the furniture in the environment. Furthermore, we introduce the application of 2D furniture free mapping for room segmentation.