Multi- and Hyper-Spectral Sensing for Autonomous Ground Vehicle Navigation
Robotic vehicles that navigate autonomously are hindered by unnecessary avoidance of soft obstacles, and entrapment by potentially avoidable obstacles. Existing sensing technologies fail to reliably distinguish hard obstacles from soft obstacles, as well as impassable thickets and other sources of entrapment. Automated materials classification through advanced sensing methods may provide a means to identify such obstacles, and from their identity, to determine whether they must be avoided. Multi- and hyper-spectral electro-optic sensors are used in remote sensing applications to classify both man-made and naturally occurring materials on the earth's surface by their reflectance spectra. The applicability of this sensing technology to obstacle identification for autonomous ground vehicle navigation is the focus of this report. The analysis is restricted to system concepts in which the multi- or hyper-spectral sensor is on-board the ground vehicle, facing forward to detect and classify obstacles ahead of the vehicle. Obstacles of interest include various types of vegetation, rocks, soils, minerals, and selected man-made materials such as paving asphalt and concrete.
- Research Organization:
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Sandia National Lab. (SNL-CA), Livermore, CA (United States)
- Sponsoring Organization:
- US Department of Energy (US)
- DOE Contract Number:
- AC04-94AL85000
- OSTI ID:
- 820893
- Report Number(s):
- SAND2003-1980; TRN: US200407%%56
- Resource Relation:
- Other Information: PBD: 1 Jun 2003
- Country of Publication:
- United States
- Language:
- English
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