Grid Map Guided Indoor 3D Reconstruction for Mobile Robots with RGB-D Sensors

This paper presents a novel automatic indoor three-dimensional (3D) scene reconstruction approach which is guided by a beforehand two-dimensional (2D) grid map. The proposed system collects only a few images with a RGB-D sensor (Kinect v2) at poses precalculated by the grid map to reconstruct a complete model of the indoor environment. To remove noises result from mirror reflection, a wall detection based cloud filtering method is proposed as preprocessing for the point clouds. Then, robot location information provided by a laser scanner is utilized as an initial guess of the point cloud registration which uses Iterative Closest Point (ICP). Finally, to maintain global consistency and improve the overall accuracy, a graph optimization strategy is applied. Experimental results are provided to show the effectiveness of the proposed approach.