Development of Kinematic 3D Laser Scanning System for Indoor Mapping and As-Built BIM Using Constrained SLAM

Abstract

The growing interest and use of indoor mapping is driving a demand for improved data-acquisition facility, efficiency and productivity in the era of the Building Information Model (BIM). The conventional static laser scanning method suffers from some limitations on its operability in complex indoor environments, due to the presence of occlusions. Full scanning of indoor spaces without loss of information requires that surveyors change the scanner position many times, which incurs extra work for registration of each scanned point cloud. Alternatively, a kinematic 3D laser scanning system, proposed herein, uses line-feature-based Simultaneous Localization and Mapping (SLAM) technique for continuous mapping. Moreover, to reduce the uncertainty of line-feature extraction, we incorporated constrained adjustment based on an assumption made with respect to typical indoor environments: that the main structures are formed of parallel or orthogonal line features. The superiority of the proposed constrained adjustment is its reduction for uncertainties of the adjusted lines, leading to successful data association process. In the present study, kinematic scanning with and without constrained adjustment were comparatively evaluated in two test sites, and the results confirmed the effectiveness of the proposed system. The accuracy of the 3D mapping result was additionally evaluated by comparison with the reference points acquired by a total station: the Euclidean average distance error was 0.034 m for the seminar room and 0.043 m for the corridor, which satisfied the error tolerance for point cloud acquisition (0.051 m) according to the guidelines of the General Services Administration for BIM accuracy.

Examination of Indoor Mobile Mapping Systems in a Diversified Internal/External Test Field

“…More in general, the evaluation of the correctness and accuracy of a point cloud can be carried out by following at least these three approaches: cloud to cloud [30], point to point [31], and cloud to feature [32,33] comparisons.…”

An Offline Coarse-To-Fine Precision Optimization Algorithm for 3D Laser SLAM Point Cloud

“…Thus, the early laser SLAM algorithms usually used 2D LiDAR as the main sensor, and the final mapping result was a 2D occupancy grid map [9][10][11]. With the development of multi-beam LiDAR and increasing demands for 3D spatial information, researchers proposed more and more 3D laser SLAM algorithms [12][13][14][15][16][17][18]. Lidar odometry and mapping (LOAM) is one of the most popular laser SLAM algorithms, which can use a rotation 2D LiDAR or multi-beam LiDAR to generate 3D point cloud [19].…”

A New Localization System for Indoor Service Robots in Low Luminance and Slippery Indoor Environment Using Afocal Optical Flow Sensor Based Sensor Fusion

“…However, these methods are inconvenient, since equipments must be installed and calibrated in advance. Therefore, consumer service robots mainly use built-in sensors such as wheel encoders, inertial sensors, mono camera, stereo camera, RGB-D camera [ 8 , 9 ] or laser scanner [ 10 , 11 ] for localization without external assistance. Among vision sensors and laser scanner, mono camera is highly attractive owing to its low cost, light weight, and low power consumption.…”