Door detection in 3D coloured point clouds of indoor environments

Abstract Door detection is becoming an increasingly important subject in building indoor modelling owing to its value in scan-to-BIM processes. This paper presents an original approach that detects open, semi-open and closed doors in 3D laser scanned data of indoor environments. The proposed technique is unique in that it integrates the information regarding both the geometry (i.e. XYZ coordinates) and colour (i.e. RGB or HSV) provided by a calibrated set of 3D laser scanner and a colour camera. In other words, our technique is developed in a 6D-space framework. The geometry-colour integration and other characteristics of our method make it robust to occlusion and variations in colours resulting from varying lighting conditions at each scanning location (e.g. specular highlights) and from different scanning locations. In addition to this paper, the authors also contribute a public dataset of real scenes along with an annotated ground truth. The dataset has varying levels of challenges and will help to assess the performance of new and existing contributions in the field. The approach proposed in this paper is tested against that dataset, yielding encouraging results.

[1]  Antonio Adán,et al.  Semantic scan planning for indoor structural elements of buildings , 2016, Adv. Eng. Informatics.

[2]  Antonio Adán,et al.  Door detection in 3D colored laser scans for autonomous indoor navigation , 2016, 2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN).

[3]  RusuRadu Bogdan,et al.  Towards 3D Point cloud based object maps for household environments , 2008 .

[4]  Pedro Arias,et al.  3D Modeling of Building Indoor Spaces and Closed Doors from Imagery and Point Clouds , 2015, Sensors.

[5]  Sven Wachsmuth,et al.  Automated Door Detection with a 3D-Sensor , 2014, 2014 Canadian Conference on Computer and Robot Vision.

[6]  高翔,et al.  Detecting, Locating and Crossing a Door for a Wide Indoor Surveillance Robot , 2013 .

[7]  Farid Melgani,et al.  Geometric model for vision-based door detection , 2014, 2014 9th International Conference on Computer Engineering & Systems (ICCES).

[8]  Burcu Akinci,et al.  Automatic Reconstruction of As-Built Building Information Models from Laser-Scanned Point Clouds: A Review of Related Techniques | NIST , 2010 .

[9]  Christopher G. Atkeson,et al.  Human-supervised control of the ATLAS humanoid robot for traversing doors , 2015, 2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids).

[10]  E. R. Davies,et al.  Machine vision - theory, algorithms, practicalities , 2004 .

[11]  Silvano Di Zenzo,et al.  A note on the gradient of a multi-image , 1986, Comput. Vis. Graph. Image Process..

[12]  Michael J. Black,et al.  Fields of Experts: a framework for learning image priors , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[13]  Senem Velipasalar,et al.  Doorway detection for autonomous indoor navigation of unmanned vehicles , 2016, 2016 IEEE International Conference on Image Processing (ICIP).

[14]  Xiaodong Yang,et al.  Robust door detection in unfamiliar environments by combining edge and corner features , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops.

[15]  G. Lazea,et al.  Classification within indoor environments using 3D perception , 2012, Proceedings of 2012 IEEE International Conference on Automation, Quality and Testing, Robotics.

[16]  Burcu Akinci,et al.  Automatic Creation of Semantically Rich 3D Building Models from Laser Scanner Data , 2013 .

[17]  John K. Tsotsos,et al.  Active Vision for Door Localization and Door Opening using Playbot: A Computer Controlled Wheelchair for People with Mobility Impairments , 2008, 2008 Canadian Conference on Computer and Robot Vision.

[18]  Markus Vincze,et al.  3D room modeling and doorway detection from indoor stereo imagery using feature guided piecewise depth diffusion , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[19]  Fazida Hanim Hashim,et al.  An automated 3D scanning algorithm using depth cameras for door detection , 2015, 2015 International Electronics Symposium (IES).

[20]  Keng Peng Tee,et al.  Automated door opening scheme for non-holonomic mobile manipulator , 2013, 2013 13th International Conference on Control, Automation and Systems (ICCAS 2013).

[21]  Matthew Derry,et al.  Automated doorway detection for assistive shared-control wheelchairs , 2013, 2013 IEEE International Conference on Robotics and Automation.

[22]  Xiang Gao,et al.  Detecting, locating and crossing a door for a wide indoor surveillance robot , 2013, 2013 IEEE International Conference on Robotics and Biomimetics (ROBIO).

[23]  Andreas Uhl,et al.  BlenSor: Blender Sensor Simulation Toolbox , 2011, ISVC.

[24]  Lei Zhang,et al.  3D Perception for Autonomous Robot Exploration , 2015, ISVC.

[25]  François Pasteau,et al.  Simple monocular door detection and tracking , 2013, 2013 IEEE International Conference on Image Processing.

[26]  Nico Blodow,et al.  Towards 3D Point cloud based object maps for household environments , 2008, Robotics Auton. Syst..

[27]  A Budroni,et al.  Automatic 3D modelling of indoor Manhattan-world scenes from laser data , 2010 .

[28]  Gang Wang,et al.  Door recognition and deep learning algorithm for visual based robot navigation , 2014, 2014 IEEE International Conference on Robotics and Biomimetics (ROBIO 2014).

[29]  Dong Hwan Kim,et al.  Context-based object recognition for door detection , 2011, 2011 15th International Conference on Advanced Robotics (ICAR).