Performance of Different SLAM Algorithms for Indoor and Outdoor Mapping Applications

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.

Graph SLAM-Based 2.5D LIDAR Mapping Module for Autonomous Vehicles

This paper proposes a unique Graph SLAM framework to generate precise 2.5D LIDAR maps in an XYZ plane. A node strategy was invented to divide the road into a set of nodes. The LIDAR point clouds are smoothly accumulated in intensity and elevation images in each node. The optimization process is decomposed into applying Graph SLAM on nodes’ intensity images for eliminating the ghosting effects of the road surface in the XY plane. This step ensures true loop-closure events between nodes and precise common area estimations in the real world. Accordingly, another Graph SLAM framework was designed to bring the nodes’ elevation images into the same Z-level by making the altitudinal errors in the common areas as small as possible. A robust cost function is detailed to properly constitute the relationships between nodes and generate the map in the Absolute Coordinate System. The framework is tested against an accurate GNSS/INS-RTK system in a very challenging environment of high buildings, dense trees and longitudinal railway bridges. The experimental results verified the robustness, reliability and efficiency of the proposed framework to generate accurate 2.5D maps with eliminating the relative and global position errors in XY and Z planes. Therefore, the generated maps significantly contribute to increasing the safety of autonomous driving regardless of the road structures and environmental factors.

Graph SLAM Built over Point Clouds Matching for Robot Localization in Tunnels

This paper presents a fully original algorithm of graph SLAM developed for multiple environments—in particular, for tunnel applications where the paucity of features and the difficult distinction between different positions in the environment is a problem to be solved. This algorithm is modular, generic, and expandable to all types of sensors based on point clouds generation. The algorithm may be used for environmental reconstruction to generate precise models of the surroundings. The structure of the algorithm includes three main modules. One module estimates the initial position of the sensor or the robot, while another improves the previous estimation using point clouds. The last module generates an over-constraint graph that includes the point clouds, the sensor or the robot trajectory, as well as the relation between positions in the trajectory and the loop closures.