Summary
We present a probabilistic technique for alignment and subsequent change detection using range sensor data. The alignment method is derived from a novel, non-rigid approach to register point clouds induced by pose-related range observations that are particularly erroneous. It allows for high scan estimation errors to be compensated distinctly, whilst considering temporally successive measurements to be correlated. Based on the alignment, changes between data sets are detected using a probabilistic approach that is capable of differentiating between likely and unlikely changes. When applied to observations containing even small differences, it reliably identifies intentionally introduced modifications.
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References
Singh, A. 1989. Digital change detection techniques using remotely-sensed data. International Journal of Remote Sensing, 10(6):989–1004.
Dai, X., Khorram, S. 1998. The effects of image misregistration on the accuracy of remotely sensed change detection. IEEE Transactions on Geoscience and Remote Sensing, 36(5):1566–1577.
Chavez, P. S., MacKinnon, D. J. 1994. Automatic detection of vegetation changes in the southwestern United States using remotely sensed images. Photogramm Eng. Remote Sensing, 60(5):571–582.
Carlotto, M. J. 1997. Detection and analysis of change in remotely sensed imagery with application to wide area surveillance. IEEE Transactions on Image Processing, 6(1):189–202.
Murakami, H., Nakagawa, K., Hasegawa, H., Shibata, T., Iwanami, E. 1999. Change detection of buildings using an airborne laser scanner. Journal of Photogrammetry and Remote Sensing, 54(2–3):148–152.
Vu, T. T., Matsuoka, M., Yamazaki, F. 2004. Lidar-based change detection of buildings in dense urban areas. IEEE 200 International Geoscience and Remote Sensing Symposium, CD-ROM, 4p, 2004.9.
Coppin, P. R., Bauer, M. E. 1996. Digital change detection in forest ecosystems with remote sensing imagery. Remote Sensing Reviews, 13(3–4):207–234.
Hsiao, K. H., Liu, J. K., Yu, M. F., Tseng, Y. H. 1999. Change detection of landslide terrains using ground-based lidar data. Proceedings of the XXth ISPRS Congress, Istanbul, Turkey.
Habib, A. F., Lee, Y.-R., Morgan, M. 2001. Surface matching and change detection using a modified Hough transformation for robust parameter estimation. Photogrammetric Record, 17(98):303–315.
Besl, P. and McKay, N. 1992. A method for registration of 3d shapes. Transactions on Pattern Analysis and Machine Intelligence, 14(2):239–256.
Chen, Y. and Medioni, G. 1992. Object modeling by registration of multiple range images. Image and Vision Computing, 10:145–155.
Chui, H., Rangarajan, A. 2000. A new point matching algorithm for non-rigid registration. Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR).
Haehnel, D., Thrun, S., Burgard, W. 2003. An extension of the ICP algorithm for modeling nonrigid objects with mobile robots. Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence (IJCAI), Acapulco, Mexico.
Whalley, M., Freed, M., Harris, R., Takahashi, M., Schulein, G., and Howlett, J. 2005. Design, integration, and flight test results for an autonomous surveillance helicopter. AHS International Specialists Meeting on Unmanned Rotorcraft, Mesa, AZ.
Press, W. H. 1988. Numerical recipes in C: the art of scientific computing. Cambridge University Press, Cambridge, New York.
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Kaestner, R., Thrun, S., Montemerlo, M., Whalley, M. (2006). A Non-rigid Approach to Scan Alignment and Change Detection Using Range Sensor Data. In: Corke, P., Sukkariah, S. (eds) Field and Service Robotics. Springer Tracts in Advanced Robotics, vol 25. Springer, Berlin, Heidelberg . https://doi.org/10.1007/978-3-540-33453-8_16
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DOI: https://doi.org/10.1007/978-3-540-33453-8_16
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