Abstract
Occupancy mapping is widely used to generate volumetric 3D environment models from point clouds, informing a robotic platform which parts of the environment are free and which are not. The selection of the parameters that govern the point cloud generation algorithms and mapping algorithms affects the process and the quality of the final map. Although previous studies have been reported in the literature on optimising major parameter configurations, research in the process to identify optimal parameter sets to achieve best occupancy mapping performance remains limited. The current work aims to fill this gap with a two-step principled methodology that first identifies the most significant parameters by conducting Neighbourhood Component Analysis on all parameters and then optimise those using grid search with the area under the Receiver Operating Characteristic curve. This study is conducted on 20 data sets with specially designed targets, providing precise ground truths for evaluation purposes. The methodology is tested on OctoMap with point clouds created by applying StereoSGBM on the images from a stereo camera. A clear indication can be seen that mapping parameters are more important than point cloud generation parameters. Moreover, up to 15% improvement in mapping performance can be achieved over default parameters.
Original language | English |
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Article number | 7004 |
Journal | Sensors |
Volume | 21 |
Issue number | 21 |
DOIs | |
Publication status | Published - 22 Oct 2021 |
Bibliographical note
Funding Information:Funding: This research was funded by University of Bath grant University Research Studentship Award-Engineering and China Scholarship Council grant No. 201706120022. The APC was funded by the Bath Open Access fund.
Funding
Funding: This research was funded by University of Bath grant University Research Studentship Award-Engineering and China Scholarship Council grant No. 201706120022. The APC was funded by the Bath Open Access fund.
Keywords
- Data sets for SLAM
- Mapping
- SLAM
ASJC Scopus subject areas
- Analytical Chemistry
- Information Systems
- Atomic and Molecular Physics, and Optics
- Biochemistry
- Instrumentation
- Electrical and Electronic Engineering
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Dataset for "Parameter Reduction and Optimisation for Point Cloud and Occupancy Mapping Algorithms"
Miao, Y. (Creator), Hunter, A. (Creator) & Georgilas, I. (Creator), University of Bath, 14 Jun 2021
DOI: 10.15125/BATH-00594
Dataset