@inproceedings{7d4ea8179d154df2a69c711d40ebfaec,
title = "An Autonomous Mapping Approach for Confined Spaces Using Flying Robots",
abstract = "Mapping a confined space with a drone-based system becomes challenging when vision sensors cannot be used due to environmental constraints. This paper presents a novel scan-matching approach based on an Iterative Closest Point algorithm that uses low-rate and low-dense scans from a LiDAR. The proposed technique only employs the horizontal layer from a 3D LiDAR to estimate the transformation matrices in a computationally efficient fashion, which is then used to generate the 3D map of the scanned environment in real-time. This is, then, complemented with a fit-for-purpose indoor navigation path-planning strategy. The method was successfully tested by mapping a confined space within a cement plant simulated environment and estimating a stockpile volume stored in that space. The volume of the reconstructed stockpile was estimated with an error as low as 3%, which matches the accuracy levels recommended by relevant regulations.",
keywords = "Autonomous, Confined spaces, Flying robots, Mapping",
author = "Ahmad Alsayed and Nabawy, {Mostafa R.A.} and Akilu Yunusa-Kaltungo and Quinn, {Mark K.} and Farshad Arvin",
note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 22th Annual Conference Towards Autonomous Robotic Systems, TAROS 2021 ; Conference date: 08-09-2021 Through 10-09-2021",
year = "2021",
doi = "10.1007/978-3-030-89177-0_33",
language = "English",
isbn = "9783030891763",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Nature",
pages = "326--336",
editor = "Charles Fox and Junfeng Gao and {Ghalamzan Esfahani}, Amir and Mini Saaj and Marc Hanheide and Simon Parsons",
booktitle = "Towards Autonomous Robotic Systems",
address = "United States",
}