Walk&Sketch: create floor plans with an RGB-D camera

Creating floor plans for large areas via manual surveying is labor-intensive and error-prone. In this paper, we present a system, Walk&Sketch, that creates floor plans of an indoor environment by a person walking through the environment at a normal strolling pace and taking videos using a consumer RGB-D camera. The method computes floor maps represented by polylines from a 3D point cloud based on precise frame-to-frame alignment. It aligns a reference frame with the floor and computes the frame-to-frame offsets from the continuous RGB-D input. Line segments at a certain height are extracted from the 3D point cloud, and are merged to form a polyline map, which can be further modified and annotated by users. The explored area is visualized as a sequence of polygons, providing users with the information on coverage. Experiments have done in various areas of an office building and have shown encouraging results.

[1]  Andrew W. Fitzgibbon,et al.  KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera , 2011, UIST.

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

[3]  Wolfram Burgard,et al.  A Tree Parameterization for Efficiently Computing Maximum Likelihood Maps using Gradient Descent , 2007, Robotics: Science and Systems.

[4]  Roland Siegwart,et al.  Orthogonal 3D-SLAM for Indoor Environments Using Right Angle Corners , 2007, EMCR.

[5]  Cyrill Stachniss,et al.  Hierarchical optimization on manifolds for online 2D and 3D mapping , 2010, 2010 IEEE International Conference on Robotics and Automation.

[6]  G. Klein,et al.  Parallel Tracking and Mapping for Small AR Workspaces , 2007, 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality.

[7]  Wolfram Burgard,et al.  Nonlinear Constraint Network Optimization for Efficient Map Learning , 2009, IEEE Transactions on Intelligent Transportation Systems.

[8]  Alberto Elfes,et al.  Using occupancy grids for mobile robot perception and navigation , 1989, Computer.

[9]  Jean-Yves Bouguet,et al.  Camera calibration toolbox for matlab , 2001 .

[10]  Ying Zhang,et al.  Real-time outline mapping for mobile blind robots , 2011, 2011 IEEE International Conference on Robotics and Automation.

[11]  Andreas Nüchter,et al.  3D Robotic Mapping - The Simultaneous Localization and Mapping Problem with Six Degrees of Freedom , 2009, Springer Tracts in Advanced Robotics.

[12]  Changchang Wu,et al.  SiftGPU : A GPU Implementation of Scale Invariant Feature Transform (SIFT) , 2007 .

[13]  Henrik I. Christensen,et al.  2D mapping of cluttered indoor environments by means of 3D perception , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[14]  Walterio W. Mayol-Cuevas,et al.  Real-Time Model-Based SLAM Using Line Segments , 2006, ISVC.

[15]  Dieter Fox,et al.  Interactive 3D modeling of indoor environments with a consumer depth camera , 2011, UbiComp '11.

[16]  Roland Siegwart,et al.  Orthogonal SLAM: a Step toward Lightweight Indoor Autonomous Navigation , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

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

[18]  Andrew W. Fitzgibbon,et al.  KinectFusion: Real-time dense surface mapping and tracking , 2011, 2011 10th IEEE International Symposium on Mixed and Augmented Reality.

[19]  Marc Levoy,et al.  Efficient variants of the ICP algorithm , 2001, Proceedings Third International Conference on 3-D Digital Imaging and Modeling.

[20]  Olivier Stasse,et al.  MonoSLAM: Real-Time Single Camera SLAM , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Andrew J. Davison,et al.  DTAM: Dense tracking and mapping in real-time , 2011, 2011 International Conference on Computer Vision.