The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B2-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 507–514, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-507-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 507–514, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-507-2020

  12 Aug 2020

12 Aug 2020

MOVING OBJECTS AWARE SENSOR MESH FUSION FOR INDOOR RECONSTRUCTION FROM A COUPLE OF 2D LIDAR SCANS

T. Wu1, B. Vallet1, C. Demonceaux2, and J. Liu3,4 T. Wu et al.
  • 1LASTIG, Univ Gustave Eiffel, ENSG, IGN, F-94160 Saint-Mandé, France
  • 2VIBOT ERL CNRS 6000, ImViA Université Bourgogne Franche-Comté, France
  • 3State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
  • 4Department of Remote Sensing and Photogrammetry and the Center of Excellence in Laser Scanning Research, Finnish Geospatial Research Institute, 02430 Masala, Finland

Keywords: Lidar processing, indoor reconstruction, visibility analysis, change detection, moving objects detection, mesh fusion, mesh mosaic

Abstract. Indoor mapping attracts more attention with the development of 2D and 3D camera and Lidar sensor. Lidar systems can provide a very high resolution and accurate point cloud. When aiming to reconstruct the static part of the scene, moving objects should be detected and removed which can prove challenging. This paper proposes a generic method to merge meshes produced from Lidar data that allows to tackle the issues of moving objects removal and static scene reconstruction at once. The method is adapted to a platform collecting point cloud from two Lidar sensors with different scan direction, which will result in different quality. Firstly, a mesh is efficiently produced from each sensor by exploiting its natural topology. Secondly, a visibility analysis is performed to handle occlusions (due to varying viewpoints) and remove moving objects. Then, a boolean optimization allows to select which triangles should be removed from each mesh. Finally, a stitching method is used to connect the selected mesh pieces. Our method is demonstrated on a Navvis M3 (2D laser ranger system) dataset and compared with Poisson and Delaunay based reconstruction methods.