Abstract
A mobile robot equipped with a single camera can take images at different locations to obtain the 3D information of the environment for navigation. The depth information perceived by the robot is critical for obstacle avoidance. Given a calibrated camera, the accuracy of depth computation largely depends on locations where images have been taken. For any given image pair, the depth error in regions close to the camera baseline can be excessively large or even infinite due to the degeneracy introduced by the triangulation in depth computation. Unfortunately, this region often overlaps with the robot’s moving direction, which could lead to collisions. To deal with the issue, we analyze depth computation and propose a predictive depth error model as a function of motion parameters. We name the region where the depth error is above a given threshold as an untrusted area. Note that the robot needs to know how its motion affect depth error distribution beforehand, we propose a closed-form model predicting how the untrusted area is distributed on the road plane for given robot/camera positions. The analytical results have been successfully verified in the experiments using a mobile robot.
This work was supported in part by the National Science Foundation under IIS-0534848 and IIS-0643298, and in part by Microsoft Corporation.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Chaumette, F., Boukir, S., Bouthemy, P., Juvin, D.: Structure from controlled motion. IEEE Transactions on Pattern Analysis and Machine Intelligence 18(5), 492–504 (1996)
Bajcsy, R.: Active perception. Proceedings of the IEEE 76(8), 996–1005 (1988)
Zavidovique, B.: First steps of robotic perception: The turning point of the 1990s. Proceedings of the IEEE 90(7), 1094–1112 (2002)
Tarabanis, K., Allen, P., Tsai, R.: A survey of sensor planning in computer vision. IEEE Transactions on Robotics and Automation 11(1), 86–104 (1995)
Mouragnon, E., Lhuillier, M., Dhome, M., Dekeyser, F., Sayd, P.: Monocular vision based slam for mobile robots. In: The 18th International Conference on Pattern Recognition, August 2006, pp. 1027–1031 (2006)
Chen, Z., Rodrigo, R., Samarabandu, J.: Implementation of an update scheme for monocular visual slam. In: International Conference on Information and Automation, December 2006, pp. 212–217 (2006)
Mortard, E., Raducanu, B., Cadenat, V., Vitria, J.: Incremental on-line topological map learning for a visual homing application. In: IEEE International Conference on Robotics and Automation, April 2007, pp. 2049–2054 (2007)
Lemaire, T., Lacroix, S.: Monocular-vision based slam using line segments. In: IEEE International Conference on Robotics and Automation, April 2007, pp. 2791–2796 (2007)
Royer, E., Bom, J., Michel Dhome, B.T., Lhuillier, M., Marmoiton, F.: Outdoor autonomous navigation using monocular vision. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, August 2005, pp. 1253–1258 (2005)
Chen, Z., Birchfield, S.T.: Qualitative vision-based mobile robot navigation. In: IEEE International Conference on Robotics and Automation, Olrando, FL, May 2006, pp. 2686–2692 (2006)
Michels, J., Saxena, A., Ng, A.: High speed obstacle avoidance using monocular vision and reinforcement learning. In: 22nd International Conference on Machine Learning, August 2005, pp. 593–600 (2005)
Song, D., Lee, H., Yi, J., Levandowski, A.: Vision-based motion planning for an autonomous motorcycle on ill-structured roads. Autonomous Robots 23(3), 197–212 (2007)
Azarbayejani, A., Pentland, A.: Recursive estimation of motion, structure, and focal length. IEEE Transactions on Pattern Analysis and Machine Intelligence 17(6), 562–575 (1995)
Jebara, T., Azarbayejani, A., Pentland, A.: 3D structure from 2D motion. IEEE Signal Processing Magazine 16(3), 66–84 (1999)
Tomasi, C., Kanade, T.: Shape and motion from image streams under orthography: a factorization method. International Journal of Computer Vision 9(2), 137–154 (1992)
Martinec, D., Pajdla, T.: 3D recontruction by fitting low-rank matrices with missing data. In: IEEE Conference on Computer Vision and Pattern Recognition, San Diego, CA, June 2005, pp. 198–205 (2005)
Brandt, S.: Closed-form solutions for affine reconstruction under missing data. In: 7th European Conference on Computer Vision, Copenhagen, Denmark, May 2002, pp. 109–114 (2002)
Hartley, R., Schaffalizky, F.: Powerfactorization: 3D reconstruction with missing or uncertain data. In: Australia-Japan Advanced Workshop on Computer Vision (September 2003)
Guilbert, N., Bartoli, A.: Batch recovery of multiple views with missing data using direct sparse solvers. In: British Machine Vision Conference, Norwich, UK (September 2003)
Anandan, P., Irani, M.: Factorization with uncertainty. International Journal of Computer Vision 49(3), 101–116 (2002)
Triggs, B.: Plane+parallax, tensors and factorization. In: 6th European Conference on Computer Vision, Dublin, Ireland, June 2000, pp. 522–538 (2000)
Irani, M., Anandan, P., Cohen, M.: Direct recovery of planar-parallax from multiple frames. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(11), 1528–1534 (2002)
Rother, C., Carlsson, S.: Linear multi view reconstruction and camera recovery using a reference plane. International Journal of Computer Vision 49(3), 117–141 (2002)
Bartoli, A., Sturm, P.: Contrained structure and motion from multiple uncalibrated views of a piecewise planar scene. International Journal of Computer Vision 52(1), 45–64 (2003)
Dellaert, F., Seitz, S.M., Thorpe, C.E., Thrun, S.: Structure from motion without correspondence. In: IEEE Conference on Computer Vision and Pattern Recognition, Hilton Head, SC, June 2000, pp. 557–564 (2000)
Chowdhury, A.K.R., Chellappa, R.: Statistical bias in 3-D reconstruction from a monocular video. IEEE Transactions on Image Processing 14(8), 1057–1062 (2005)
Hartley, R., Zisserman, A.: Multiple View Geometry in computer vision. Cambridge University Press, Cambridge (2003)
Bouguet, J.-Y.: Camera calibration toolbox for matlab (2007), http://www.vision.caltech.edu/bouguetj/calib_doc/index.html
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Song, D., Lee, H., Yi, J. (2009). On the Analysis of the Depth Error on the Road Plane for Monocular Vision-Based Robot Navigation. In: Chirikjian, G.S., Choset, H., Morales, M., Murphey, T. (eds) Algorithmic Foundation of Robotics VIII. Springer Tracts in Advanced Robotics, vol 57. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00312-7_19
Download citation
DOI: https://doi.org/10.1007/978-3-642-00312-7_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-00311-0
Online ISBN: 978-3-642-00312-7
eBook Packages: EngineeringEngineering (R0)