Obstacle Detection of Autonomous Land Vehicles for Geological Prospecting

Article Preview

Abstract:

The paper mainly introduces the method of obstacle detection of autonomous land vehicle for geological prospecting. According to the categories and characteristic of obstacles, choosing the applicable sensor is the fundamental of accurately detecting obstacles. Data processing, including information collection, information processing and collecting characteristic parameter obstacle, is the difficulty for accurately detecting obstacles.

You might also be interested in these eBooks

Info:

Periodical:

Advanced Materials Research (Volumes 774-776)

Pages:

1573-1576

Citation:

Online since:

September 2013

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2013 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] BRAID D, BROGGI A, SCHMIEDEL G. The TerraMax autonomous vehicle concludes the 2005 DARPA Grand Challenge, F, 2006 [C]. IEEE.

DOI: 10.1109/ivs.2006.1689683

Google Scholar

[2] SHI X, MANDUCHI R. A study on Bayes feature fusion for image classification, F, 2003 [C]. IEEE.

Google Scholar

[3] MANDUCHI R, CASTANO A, TALUKDER A, et al. Obstacle detection and terrain classification for autonomous off-road navigation [J]. Autonomous Robots, 2005, 18(1): 81-102.

DOI: 10.1023/b:auro.0000047286.62481.1d

Google Scholar

[4] ROSENBLUM M, GOTHARD B. A high fidelity multi-sensor scene understanding system for autonomous navigation, F, 2000 [C]. IEEE.

DOI: 10.21236/ada444473

Google Scholar

[5] RANKIN A, HUERTAS A, MATTHIES L. Nighttime negative obstacle detection for off-road autonomous navigation, F, 2007 [C]. Citeseer.

Google Scholar

[6] MATTHIES L, RANKIN A. Negative obstacle detection by thermal signature, F, 2003 [C]. IEEE.

Google Scholar

[7] HECKMAN N, LALONDE J F, VANDAPEL N, et al. Potential negative obstacle detection by occlusion labeling, F, 2007 [C]. IEEE.

DOI: 10.1109/iros.2007.4398970

Google Scholar

[8] MATTHIES L, GRANDJEAN P. Stochastic performance, modeling and evaluation of obstacle detectability with imaging range sensors [J]. Robotics and Automation, IEEE Transactions on, 1994, 10(6): 783-92.

DOI: 10.1109/70.338533

Google Scholar

[9] COLWELL R N, ULABY F, SIMONETT D, et al. Manual of remote sensing. Volume 2. Interpretation and applications [M]. American Society of Photogrammetry, (1983).

Google Scholar

[10] MATTHIES L, BERGH C, CASTANO A, et al. Obstacle detection in foliage with ladar and radar [J]. Robotics Research, 2005, 291-300.

DOI: 10.1007/11008941_31

Google Scholar