JOURNAL BROWSE
Search
Advanced SearchSearch Tips
Mobile Robot Path Finding Using Invariant Landmarks
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Mobile Robot Path Finding Using Invariant Landmarks
Sharma, Kajal;
  PDF(new window)
 Abstract
This paper proposes a new path-finding scheme using viewpoint-invariant landmarks. The scheme introduces the concept of landmark detection in images captured with a vision sensor attached to a mobile robot, and provides landmark clues to determine a path. Experiment results show that the scheme efficiently detects landmarks with changes in scenes due to the robot`s movement. The scheme accurately detects landmarks and reduces the overall landmark computation cost. The robot moves in the room to capture different images. It can efficiently detect landmarks in the room from different viewpoints of each scene. The outcome of the proposed scheme results in accurate and obstacle-free path estimation.
 Keywords
Landmark;Path finding;Image processing;
 Language
English
 Cited by
 References
1.
M. Kaess, A. Ranganathan, F. Dellaert,"iSAM: Incremental smoothing and mapping," IEEE Trans. on Robotics, vol. 24, no. 6, pp. 1365-1378, 2008. crossref(new window)

2.
R. Manduchiet. al., "Obstacle detection and terrain classification for autonomous off-road navigation,"Autonomous Robots, vol. 18, no. 1, pp. 81-102, 2005. crossref(new window)

3.
G. Grisetti, G. Stachniss, andW. Burgard,"Non-linear constraint network optimization for efficient map learning,"IEEE Trans. on Intelligent Transportation systems, vol. 10, no. 3, pp. 428-439, 2009. crossref(new window)

4.
K. Sharma, I. Moon, andS. Kim, "Extraction of visual landmarks using improved feature matching technique for stereo vision applications,"IETE Technical Review, vol. 29, no. 6, pp. 473-481, 2012. crossref(new window)

5.
J. Stuhmer,S. Gumhold, andD. Cremers,"Real-time dense geometry from a handheld camera," Proc. DAGM Symposium on Pattern Recognition, 2010.

6.
D. Nister,"Preemptive ransac for live structure and motion estimation," Machine Vision and Applications, vol. 16, no. 5, pp. 321-329, 2005. crossref(new window)

7.
K. Koeser,B. Bartczak, and R. Koch, "An analysis-by-synthesis camera tracking approach based on free-form surfaces," Proc. German Conf. on Pattern Recognition, pp. 121-131, 2007.

8.
K. Nagataniet. al., "Three-dimensional localization and mapping for mobile robot in disaster environments," Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. 3,pp. 3112-3117, 2003.

9.
K. Sharma, S. Kim, and M. Singh, "An improved feature matching technique for stereo vision applications with the use of self-organizing map," Int. J. of Prec. Eng. and Manufac., vol. 13, no. 8, pp. 1359-1368, 2012. crossref(new window)

10.
L. Paz et. al. "Large-scale 6-DOF SLAM with stereoin-hand,"IEEE Trans. Robotics,vol. 24, no. 5, pp. 946-957, 2008. crossref(new window)

11.
A. Comport, E. Malis, and P. Rives, "Real-time quadrifocal visual odometry," Int. J Robotics Research, vol. 29, no. (2-3), pp. 245-266, 2010. crossref(new window)

12.
P. Henry et. al., "RGB-D mapping: Using depth cameras for dense 3D modeling of indoor environments,"Proc. Intl. Symp. on Experimental Robotics (ISER), 2010.

13.
P. Henry et. al., "RGB-D mapping: Using Kinect-style depth cameras for dense 3D modelling of indoor environments," TheInt. J. of Robotics Research, vol. 31, no. 5, pp. 647-663, 2012. crossref(new window)

14.
W. Zhou, J. MirO, and G. Dissanayake, "Information-efficient 3-D visual SLAM for unstructured domains," IEEE Trans. on Robotics vol. 24, no. 5, pp. 1078-1087, 2008. crossref(new window)

15.
D. Marzorati, M. Matteucci, and D.Sorrenti, "Particle-based sensor modeling for 3D-vision SLAM,"Proc. IEEE Int. Conf. on Robotics and Automation, Roma, pp. 4801-4806, 2007.

16.
C. Schmid and R. Mohr, "Local grayvalue invariants for image retrieval," IEEE Trans. on Pattern Analysis and MachineIntelligence, vol. 19, no. 5, pp. 530-534, 1997. crossref(new window)

17.
J. Shi and C. Tomasi, "Good Features to Track," Proc. of the 9th IEEE Conference on Computer Vision and PatternRecognition, pp. 593-600, 1994.

18.
D. G. Lowe, "Distinctive Image Features from ScaleInvariant Keypoints," International Journal of Computer Vision, vol. 60, no. 2, pp. 91-110 (2004). crossref(new window)

19.
J. L. B. Claraco, "Development of Scientific Applications with the Mobile Robot Programming Toolkit (MRPT)," Machine Perception and Intelligent Robotics Laboratory, University of Malaga, 2010.