Segmentation of Indoor Mapping Point Clouds Applied to Crime Scenes Reconstruction

Data acquisition in forensics science must be performed in a fast and an efficient way, so that the data acquired is maximized at the same time that disturbance and time on the scene are minimized. For this reason, the use of indoor mapping systems appears as a key solution, in contrast with static systems, either laser or photogrammetry based, in which representing big and complex scenes requires acquisition from a high number of positions, and long-time dedication for data processing. This paper presents a methodology for the segmentation of point clouds acquired with a mobile indoor mapping system, and their conversion to 3-D models in CAD format, based on parameterized geometric elements from the scene. This way, all the information required in forensic sciences is stored in an adequate digital format, enabling its availability in the future, and minimizing time dedication in both data acquisition and processing steps.

[1]  R. Fabio.  From point cloud to surface the modeling and visualization problem , 2003 .

[2]  Fabio Remondino.  From point cloud to surface , 2003 .

[3]  Nico Blodow,et al.  Close-range scene segmentation and reconstruction of 3D point cloud maps for mobile manipulation in domestic environments , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[4]  Reinhard Klein,et al.  Efficient RANSAC for Point‐Cloud Shape Detection , 2007, Comput. Graph. Forum.

[5]  Diego Gonzalez-Aguilera,et al.  Forensic Terrestrial Photogrammetry from a Single Image , 2009, Journal of forensic sciences.

[6]  Pedro Arias,et al.  Low-cost aerial unit for outdoor inspection of building façades , 2013 .

[7]  C. Biscari,et al.  Medical Applications , 2016 .

[8]  Pedro Arias,et al.  Review of mobile mapping and surveying technologies , 2013 .

[9]  Hobart R. Everett,et al.  Where am I?" sensors and methods for mobile robot positioning , 1996 .

[10]  Ian T. Jolliffe,et al.  Graphical Representation of Data Using Principal Components , 1986 .

[11]  Avideh Zakhor,et al.  Classifying urban landscape in aerial LiDAR using 3D shape analysis , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[12]  John Oliensis,et al.  A Critique of Structure-from-Motion Algorithms , 2000, Comput. Vis. Image Underst..

[13]  T. Rabbani,et al.  SEGMENTATION OF POINT CLOUDS USING SMOOTHNESS CONSTRAINT , 2006 .

[14]  Ioannis Stamos,et al.  AVENUE: Automated site modeling in urban environments , 2001, Proceedings Third International Conference on 3-D Digital Imaging and Modeling.

[15]  D. Lichti,et al.  CLASSIFICATION AND SEGMENTATION OF TERRESTRIAL LASER SCANNER POINT CLOUDS USING LOCAL VARIANCE INFORMATION , 2006 .

[16]  G. Sithole,et al.  Recognising structure in laser scanning point clouds , 2004 .

[17]  Pedro Arias,et al.  Semantic as-built 3d models including shades for the evaluation of solar influence on buildings , 2013 .

[18]  R Sader,et al.  Three-dimensional recording of the human face with a 3D laser scanner. , 2006, Journal of plastic, reconstructive & aesthetic surgery : JPRAS.

[19]  Yorick Wilks,et al.  Extracting relational facts for indexing and retrieval of crime-scene photographs , 2003, Knowl. Based Syst..

[20]  R. Cipolla,et al.  Toward Automatic Bloodspatter Analysis in Crime Scenes , 2006 .

[21]  Fernando Matía,et al.  Extraction of Geometrical Features in 3D Environments for Service Robotic Applications , 2008, HAIS.

[22]  George Vosselman,et al.  Segmentation of point clouds using smoothness constraints , 2006 .

[23]  Avideh Zakhor,et al.  Image augmented laser scan matching for indoor dead reckoning , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[24]  Giovanna Sansoni,et al.  State-of-The-Art and Applications of 3D Imaging Sensors in Industry, Cultural Heritage, Medicine, and Criminal Investigation , 2009, Sensors.

[25]  Chang-Tsun Li,et al.  New Technologies for Digital Crime and Forensics: Devices, Applications, and Software , 2011 .

[26]  R. Reulke,et al.  Remote Sensing and Spatial Information Sciences , 2005 .

[27]  G. Sithole,et al.  Automatic structure detection in a point-cloud of an urban landscape , 2003, 2003 2nd GRSS/ISPRS Joint Workshop on Remote Sensing and Data Fusion over Urban Areas.

[28]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[29]  Ramesh C. Jain,et al.  Segmentation through Variable-Order Surface Fitting , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[30]  Dieter Fritsch,et al.  Photogrammetric Point Cloud Collection with Multi-camera Systems , 2012, EuroMed.

[31]  José Luis Lerma,et al.  Unsupervised robust planar segmentation of terrestrial laser scanner point clouds based on fuzzy clustering methods , 2008 .

[32]  Lucila Ohno-Machado,et al.  Medical Applications , 2020, Believing Your Ears: Examining Auditory Illusions.