Abstract
Indoor and outdoor mapping studies can be completed relatively quickly, depending on the developments in Mobile Mapping Systems. Especially in indoor environments where high accuracy GNSS positions cannot be used, mapping studies can be carried out with SLAM algorithms. Although there are many different SLAM algorithms in the literature, each can produce results with different accuracy according to the mapped environment. In this study, 3D maps were produced with LOAM, A-LOAM, and HDL Graph SLAM algorithms in different environments such as long corridors, staircases, and outdoor environments, and the accuracies of the maps produced with different algorithms were compared. For this purpose, a mobile mapping platform using Velodyne VLP-16 LIDAR sensor was developed, and the odometer drift, which causes loss of accuracy in the data collected, was minimized by loop closure and plane detection methods. As a result of the tests, it was determined that the results of the LOAM algorithm were not as accurate as those of the A-LOAM and HDL Graph SLAM algorithms. Both indoor and outdoor environments and the A-LOAM results’ accuracy were two times better than HDL Graph SLAM results.
Highlights
With the development of mobile mapping technology, fast and high accuracy mapping and precise 3D modelling have become possible
Simultaneous Localization and Mapping (SLAM) based approaches are sometimes used in the literature for this purpose
Algorithms using only LIDAR data are based on matching the point cloud common features in the t-time with the point cloud features extracted from the t − 1 time
Summary
With the development of mobile mapping technology, fast and high accuracy mapping and precise 3D modelling have become possible. The main challenge for mapping the environment with a mobile mapping system is calculating the position and orientation of the LIDAR. Only point cloud data from LIDAR is sufficient to solve this problem, while in others, additional sensor data is needed. Algorithms using only LIDAR data are based on matching the point cloud common features in the t-time with the point cloud features extracted from the t − 1 time. Algorithms that solve this problem using LIDAR and IMU receive the rotation and acceleration data of LIDAR from the IMU and create a point cloud
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