Preproessing and modeling for visual-based 3D indoor scene reconstruction

In this paper, we present a visual-based method for 3D modeling of indoor scene via mobile robot equipped with a depth camera, which can both acquire color image and dense point cloud. The raw data obtained from depth camera are noisy and non-uniformed, so a set of preprocessing methods, which consists down-sampling with volumetric pixel grid filter, statistical-based outlier removal, moving least square-based interpolation, is conducted to enhance and consolidate the data. A combination of scale invariant feature transform (SIFT) features and iterative closet point (ICP) are performed to estimate the pose of robot for frame alignment. The matched SIFT feature pairs calculated on color images of two frames are used to compute a rigid transformation matrix, which is considered as the initial transformation matrix estimation of ICP algorithm. A dense map is built by aligning of multiple frames, and a compact surface model is achieved by surface reconstruction on dense map using greedy triangulation method. Experiment result showed the method is easy to apply for indoor scene reconstruction and can be executed in nearly real time.

[1]  Richard Szeliski,et al.  Reconstructing building interiors from images , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[2]  Leonidas J. Guibas,et al.  Uncertainty and Variability in Point Cloud Surface Data , 2004, PBG.

[3]  Paul J. Besl,et al.  A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Tom Duckett,et al.  3D modeling of indoor environments by a mobile robot with a laser scanner and panoramic camera , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[5]  Marc Alexa,et al.  Computing and Rendering Point Set Surfaces , 2003, IEEE Trans. Vis. Comput. Graph..

[6]  Andrew J. Davison,et al.  Real-time simultaneous localisation and mapping with a single camera , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[7]  Charlie C. L. Wang,et al.  Iterative Consolidation of Unorganized Point Clouds , 2012, IEEE Computer Graphics and Applications.

[8]  Paul Newman,et al.  Outdoor SLAM using visual appearance and laser ranging , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[9]  Dieter Fox,et al.  RGB-D mapping: Using Kinect-style depth cameras for dense 3D modeling of indoor environments , 2012, Int. J. Robotics Res..

[10]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[11]  Zoltan-Csaba Marton,et al.  On fast surface reconstruction methods for large and noisy point clouds , 2009, 2009 IEEE International Conference on Robotics and Automation.

[12]  Masahiro Tomono.  Robust 3D SLAM with a stereo camera based on an edge-point ICP algorithm , 2009, 2009 IEEE International Conference on Robotics and Automation.

[13]  Andrew W. Fitzgibbon,et al.  KinectFusion: real-time dynamic 3D surface reconstruction and interaction , 2011, SIGGRAPH '11.

[14]  Holger H. Rapp,et al.  Experimental and Theoretical Investigation of Correlating TOF-Camera Systems , 2007 .

[15]  Dieter Fox,et al.  RGB-D Mapping: Using Depth Cameras for Dense 3D Modeling of Indoor Environments , 2010, ISER.