Full text: Proceedings, XXth congress (Part 4)

nbul 2004 
ver, using 
n optimal 
1ation, in 
ation and 
(4) 
& 6 
(6) 
() 
(8) 
S 
ints 
Ls, 0,0,5) 
0,0,K) 
oints 
on can be 
tested on 
IMP data, 
ation data, 
)04 Mars 
  
site 
jence and 
ime. Up to 
erated from 
the MER-B 
yre than 43 
-A (Figure 
n made for 
| maps for 
ille, Fram. 
  
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B4. Istanbul 2004 
Most of the above algorithms is implemented in a C++ based 
software called “MarsMapper” developed by the Mapping and 
GIS Laboratory, which has maximally automated map making 
capabilities. It can also assistant human operators for cross-tie 
point selection and rover localisation 
al 
Figure 16. Traverse map of Meridiana Planum site 
S. CONCLUSIONS 
We have introduced an approach to the making of terrain maps 
from descent imagery with vertical parallax configuration. For 
robotic stereo imagery, we have used an interest point-based 
matching and verification method to registering images in real 
time and found a dual polynomial model for DEM interpolation 
in close-range photogrammetry for Martian terrain. Cross-site 
landmark extraction and matching is explored. Mars mapping is 
maximally automated while rover localization is semi- 
automated. 
ACKNOWLEDGEMENTS 
This research is supported by JPL/NASA and conducted at the 
Mapping and GIS Laboratory of The Ohio State University. 
REFERENCES 
Atiya, S. and G. Hager, 1993. Real-time vision-based robot 
localization. IEEE Transactions on Robotics and Automation, 
9(6), pp. 785-800 
Bell, J. F., III; S. Squyres, and et al., 2003. Mars Exploration 
Rover Athena Panoramic Camera (Pancam) investigation. J. 
Geophys. Res.-Planet, 108(E12). 
Betke, M. and L. Gurvits, 1997. Mobile robot localization using 
landmarks. IEEE Trans. Robotics and Automation, 13(2), pp. 
251-263 
Brown, M.Z., D. Burschka, and G. Hager, 2003. Advances in 
computational stereo. IEEE Trans. Pattern Analysis and 
Machine Intelligence, 25(8), pp. 993-1008 
Cozman, F., E. Krotkov, and C. Guestrin, 2000. Outdoor visual 
position estimation for planetary rovers. Autonomous Robots, 
9(2), pp. 135-150 
Davison, A.J. and D. Murray, 2002. Simultaneous localization 
and map-building using active vision. IEEE Trans. Pattern 
Analysis and Machine Intelligence, 24(7), pp. 865-880 
Desouza, G.N. and A. Kak, 2002. Vision for mobile robot 
navigation: a survey. IEEE Trans. Pattern Analysis and 
Machine Intelligence, 24(2), pp. 237-267 
Fürstner, W., 1986. A feature based correspondence algorithm 
for image matching. Intl. Arch. Photogramm. and Remote 
Sensing, 26, pp. 150-166 
  
      
Jensfelt, P. and S. Kristensen, 2001. Active global localization 
for a mobile robot using multiple hypothesis tracking. IEEE 
Trans. Robotics and Automation, 17(5), pp. 748-760 
Kriegman, D.J., E. Triendl, and T. Binford, 1989. Stereo vision 
and navigation in buildings for mobile robots. IEEE Trans. 
Robotics and Automation, 5(6), pp. 792-803 
Krotkov, E. and R. Hoffman, 1994. Terrain mapping for a 
walking planetary rover. IEEE Trans. Robotics and Automation, 
10(6), pp. 728-739 
Leonard, J.J. and H. Durrant-Whyte, 1991. Mobile robot 
localization by tracking geometric beacons. IEEE Trans. 
Robotics and Automation, 7(3), pp. 376-382 
Li, R., F. Ma, F. Xu, and et al., 2002. Localization of Mars 
rovers using descent and surface-based image data. J. Geophys. 
Res.-Planet, 107(E11) 
Maki, J. N., J. Bell, and et al., 2003. Mars Exploration Rover 
engineering cameras. J. Geophys. Res.-Planet, 108(E12) 
Mouaddib, E.M. and B. Marhic, 2000. Geometrical matching 
for mobile robot localization. IEEE Trans. Robotics and 
Automation, 16(5), pp. 542-552 
Ohta, Y. and T. Kanade, 1985. Stereo by intra- and inter- 
scanline search using dynamic programming. IEEE Trans. 
Pattern Analysis and Machine Intelligence, 7(2), pp. 139-154 
Olson, C.F., 2000. Probabilistic self-localization for mobile 
robots. IEEE Trans. Robotics and Automation, 16(1), pp. 55-66 
Olson, Clark F., 2002. Selecting landmarks for localization in 
natural terrain. Autonomous Robots, 12(2), pp. 201-210 
Peleg, S. and J. Herman, 1997. Panoramic mosaics by manifold 
projection. Proc. IEEE Conf. Computer Vision and Pattern 
Recognition, pp. 338-343 
Peleg, S., M. Ben-Ezra, and Y. Pritch, 2001. Omnistereo: 
panoramic sterco imaging. IEEE Trans. Pattern Analysis and 
Machine Intelligence, 23(3), pp. 279-290 
Roy, S. and I. Cox, 1998. A maximum-flow formulation of the 
N-camera stereo correspondence problem. Proceedings of the 
International Conference on Computer Vision, Bombay, India, 
pp. 492-499 
Scharstein, D. and R. Szeliski, 2002. A taxonomy and 
evaluation of dense two-frame stereo correspondence 
algorithms. International Journal of Computer Vision, 47(1), pp. 
7-42 
Shimshoni, L, 2002. On mobile robot localization from 
landmark bearings. IEEE Trans. Robotics and Automation, 
18(6), pp. 971-976 
Sutherland, K.T. and W. Thompson, 1994. Localizing in 
unstructured environments: dealing with the errors. IEEE Trans. 
Robotics and Automation, 10(6), pp. 740-754 
Szeliski, R., 1996. Video mosaics for virtual environments. 
IEEE Computer Graphics and Applications, 16(2), pp. 22-30 
Wang, Zizuo, 1990. Principles of Photogrammetry. Publishing 
House of Surveying and Mapping, pp. 452-456 
1047 
 
	        
Waiting...

Note to user

Dear user,

In response to current developments in the web technology used by the Goobi viewer, the software no longer supports your browser.

Please use one of the following browsers to display this page correctly.

Thank you.