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, gen...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2021-08, Vol.21 (16), p.5340
Main Authors: Prados Sesmero, Carlos, Villanueva Lorente, Sergio, Di Castro, Mario
Format: Article
Language:English
Subjects:
Algorithms
calibration
Cameras
Communication
environmental reconstruction
graph SLAM
Localization
point cloud
Position sensing
Radiation
registration
robot surveillance
Robots
Sensors
Surveillance
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Summary: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.
ISSN:1424-8220
1424-8220
DOI:10.3390/s21165340