Terrain Characterization and Feature Extraction for Automated Convoys. Martin, S. M., Dawkins, J. J., Travis, W. E., & Bevly, D. M. In pages 256–265, September, 2010. ![Terrain Characterization and Feature Extraction for Automated Convoys [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper abstract bibtex Autonomous ground vehicle systems require numerous sensors in order to navigate through hazardous environments. In order for a single vehicle to detect obstacles or hazards and plan its path, it is not uncommon to see an autonomous vehicle with several LiDARs, cameras, and other specialized sensors. Passing information from the leading vehicle allows fewer sensors to be used on following vehicles in the convoy. The sharing of sensor information does raise an issue in terms of data management and storage. Passing raw measurement data from sensors such as a LiDAR can quickly become a computational burden. Instead it is more desirable to pass only the information that is pertinent to the following vehicle. The focus of this work is to develop methodologies to evaluate the terrain and extract features along the vehicle path. Of particular interest are those features which can be hazardous to a following vehicle, or those features which can aid in the planning of the following vehicles path. Ground scans from a LiDAR are used to identify objects which help determine the most appropriate path for the follower to take. The roughness of the terrain is characterized using Power Spectral Density (PSD) and Root mean squared elevation (RMSE). An algorithm based on the Wavelet transform is developed to identify important features which can be used to aid in the vehicle navigation.
@inproceedings{martin_terrain_2010,
title = {Terrain {Characterization} and {Feature} {Extraction} for {Automated} {Convoys}},
url = {http://www.ion.org/publications/abstract.cfm?jp=p&articleID=9153},
abstract = {Autonomous ground vehicle systems require numerous sensors in order to navigate through hazardous environments. In order for a single vehicle to detect obstacles or hazards and plan its path, it is not uncommon to see an autonomous vehicle with several LiDARs, cameras, and other specialized sensors. Passing information from the leading vehicle allows fewer sensors to be used on following vehicles in the convoy. The sharing of sensor information does raise an issue in terms of data management and storage. Passing raw measurement data from sensors such as a LiDAR can quickly become a computational burden. Instead it is more desirable to pass only the information that is pertinent to the following vehicle. The focus of this work is to develop methodologies to evaluate the terrain and extract features along the vehicle path. Of particular interest are those features which can be hazardous to a following vehicle, or those features which can aid in the planning of the following vehicles path. Ground scans from a LiDAR are used to identify objects which help determine the most appropriate path for the follower to take. The roughness of the terrain is characterized using Power Spectral Density (PSD) and Root mean squared elevation (RMSE). An algorithm based on the Wavelet transform is developed to identify important features which can be used to aid in the vehicle navigation.},
language = {en},
urldate = {2024-06-20},
author = {Martin, S. M. and Dawkins, J. J. and Travis, W. E. and Bevly, D. M.},
month = sep,
year = {2010},
pages = {256--265},
}
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