# Performance of Different SLAM Algorithms for Indoor and Outdoor Mapping Applications
###### 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. SLAM; indoor mapping; outdoor mapping; LIDAR +
Footnote †: journal: Applied System  
metric-based matching algorithm to estimate the robot planar displacement by matching dense two-dimensional range scans. They have improved the classical ICP method in robustness, precision, convergence rate, and computation time. [PERSON] et al. [9] used six degrees of freedom, namely x, y, and z coordinates and the roll, pitch, and yaw angles for localization and mapping of robots. They have combined ICP scan matching with a loop detection model. [PERSON] et al. [10] developed a real-time LIDAR inertial odometry system (RTLIO) to generate high precision and high-frequency odometry system for the feedback control of UAVs in an indoor environment. They stated that RTLIO could outperform ALOAM and LOAM by exhibiting a shorter time delay and greater position accuracy. [PERSON] et al. [11] presented a FastSLAM algorithm that recursively estimates the full posterior distribution over robot pose and landmark locations yet scales logarithmically with the number of landmarks in the map. [PERSON] et al. [12] designed a mobile LIDAR scanning system for extracting the canopy morphology and tree crown parameters. They have integrated LIDAR with IMU and GNSS sensors. BLAM is based on the loop closure algorithm and uses ICP as the point cloud matching method [3]. For the loop closure algorithm to work in BLAM, the starting and ending points must be within the specified tolerance limits. The LOAM algorithm presented in [1] used both low drift and low computational complexity without high accuracy ranging or inertial measurements. The main principle of LOAM is dividing the simultaneous localization and mapping problem into two algorithms. One algorithm performs odometry to estimate the velocity, while another performs fine matching and registration of point cloud in real-time. The authors have stated that motion estimation drift can be fixed by integrating the loop closure methods. A-LOAM is an Advanced implementation of LOAM, which uses the Eigen and Ceres Solver framework to simplify code structure [2]. The Graph-based SLAM approach reduces raw sensor data to a simplified prediction problem [13]. The HDL Graph SLAM is an advanced implementation of 3D Graph SLAM with NDT scan matching-based odometry estimation and loop detection [14]. With the loop closure algorithm, HDL Graph SLAM provides the distribution of accumulated errors depending on the distance. It also supports GPS and IMU sensor data and can perform floor plane detection in indoor mapping applications. With the Interactive SLAM algorithm developed by [PERSON] et al. [15], it is possible to correct the drifts manually or automatically in the 3D maps. Corrections can be made using loop closure algorithms in the data collected with any SLAM-based mobile mapping system.