Abstract
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.
Highlights
1.1 Previous WorksFor indoor static scene reconstruction, the related work can be categorized into two research areas: moving objects analysis and 3D reconstruction
(2) Visibility analysis (Underwood et al, 2013) uses the sensor information to remove the objects that are volumetrically inconsistent between scans
There are four steps in our pipeline shown in Figure 1: sensor mesh generation including pre-processing(cf Section 3.1) and mesh generation(cf Section 3.2), moving objects detection and removal introduced in Section 3.3, triangle selection using a boolean optimization, detail is in Section 3.4.1, and mesh pieces stitching is in Section 3.4.2 respectively
Summary
1.1 Previous WorksFor indoor static scene reconstruction, the related work can be categorized into two research areas: moving objects analysis and 3D reconstruction. Moving objects analysis can be based on images(Tron, Vidal, 2007) or Lidar (Schauer, Nuchter, 2018) points clouds. 1.1.1 Moving Objects Analysis The methods can be divided into two groups:. (1) Motion flow relies on ICP (Iterated Closet Point) using point correspondence to analyze the velocity of moving objects (Pomerleau et al, 2014). Simultaneous localization and mapping with moving object tracking method is mainly used in robot scanning (Wang, Thorpe, 2002). Several point based data structures such as Voxel (Andreasson et al, 2007; Schauer, Nuchter, 2018) and OctoMap (Gehrung et al, 2019) have been proposed to improve the performance of the ray tracing method
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