Abstract
Abstract. Indoor 3D mapping provides a useful three-dimensional structure via an indoor map for many applications. To acquire highly efficient and relatively accurate mapping for large-scale GPS/GNSS-denied scene, we present an upgraded backpacked laser scanning system and a car-mounted indoor mobile laser scanning system. The systems provide both 3D laser scanning point cloud and camera images. In this paper, a simultaneous extrinsic calibration approach for multiple multi-beam LIDAR and multiple cameras is also proposed using the Simultaneous Localization and Mapping (SLAM)-based algorithm. The proposed approach uses the SLAM-based algorithm to achieve a large calibration scene using mobile platforms, registers an acquired multi-beam LIDAR point cloud to the terrestrial LIDAR point cloud to acquire denser points for corner feature extraction, and finally achieves simultaneous calibration. With the proposed mapping and calibration algorithms, we can provide centimetre-lever coloured point cloud with relatively high efficiency and accuracy.
Highlights
In recent years, indoor three-dimensional (3D) map building becomes a hot research topic
To acquire highly efficient and relatively accurate mapping for large-scale GPS/GNSS-denied scene, we present an upgraded backpacked laser scanning system and a car-mount indoor mobile laser scanning system
To acquire denser points for feature extraction, a terrestrial laser scanner scans the complete calibration scene acquired by the multi-beam LiDAR and registers it to the point cloud
Summary
Indoor three-dimensional (3D) map building becomes a hot research topic. The traditional ways to get a 3D structural model of a building mainly rely on artificial hand-drawing or reconstructing based on design data, and these ways have limitations of manual error involved, low efficiency Another solution is registering multi-scans from high precision Terrestrial laser scanner and RGB-D camera, and these ways are with low efficacy, and not suitable for indoor environment with multiple corners. To acquire denser points for feature extraction, a terrestrial laser scanner scans the complete calibration scene acquired by the multi-beam LiDAR and registers it to the point cloud. This approach works well on both regular and fisheye lens camera images. Along with 3D mobile mapping process, a coloured 3D point cloud can be provided
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