A 3D obstacle detection system for a complex mobile robot in a hazardous underground tunnel environment. Suarez, C. W. Master's thesis, May, 2019. Accepted: 2019-10-25T14:56:12Z
A 3D obstacle detection system for a complex mobile robot in a hazardous underground tunnel environment [link]Paper  doi  abstract   bibtex   
This thesis presents a 3D obstacle detection system developed for use in the H-Canyon Air Exhaust (HCAEX) tunnel project. The HCAEX tunnel is a harsh environment with positive, negative, and hanging obstacles along with a muddy uneven floor. The mobile platform developed to explore this tunnel is highly complex and requires advanced knowledge of its state relative to the environment. A LiDAR sensor was identified and a Robot Operating System (ROS) package was developed to detect obstacles in 3D while accounting for the challenges presented by the project. Tests were performed in two outdoor environments and an HCAEX mock tunnel environment. Results showed that the obstacle detection system correctly identified obstacles in the environments at both roll and pitch states up to 45°, though further refinement and implementation can be performed.
@mastersthesis{suarez_3d_2019,
	title = {A {3D} obstacle detection system for a complex mobile robot in a hazardous underground tunnel environment},
	url = {https://repositories.lib.utexas.edu/handle/2152/77434},
	abstract = {This thesis presents a 3D obstacle detection system developed for use in the H-Canyon Air Exhaust (HCAEX) tunnel project.  The HCAEX tunnel is a harsh environment with positive, negative, and hanging obstacles along with a muddy uneven floor.  The mobile platform developed to explore this tunnel is highly complex and requires advanced knowledge of its state relative to the environment.  A LiDAR sensor was identified and a Robot Operating System (ROS) package was developed to detect obstacles in 3D while accounting for the challenges presented by the project.  Tests were performed in two outdoor environments and an HCAEX mock tunnel environment.  Results showed that the obstacle detection system correctly identified obstacles in the environments at both roll and pitch states up to 45°, though further refinement and implementation can be performed.},
	language = {en},
	urldate = {2020-05-08},
	author = {Suarez, Christopher William},
	month = may,
	year = {2019},
	doi = {http://dx.doi.org/10.26153/tsw/4523},
	doi = {http://dx.doi.org/10.26153/tsw/4523},
	note = {Accepted: 2019-10-25T14:56:12Z},
}

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