NDLI: A Marked Point Process for Modeling Lidar Waveforms

Content Provider	IEEE Xplore Digital Library
Author	Mallet, C. Lafarge, F. Roux, M. Soergel, U. Bretar, F. Heipke, C.
Copyright Year	1992
Abstract	Lidar waveforms are 1-D signals representing a train of echoes caused by reflections at different targets. Modeling these echoes with the appropriate parametric function is useful to retrieve information about the physical characteristics of the targets. This paper presents a new probabilistic model based upon a marked point process which reconstructs the echoes from recorded discrete waveforms as a sequence ofparametric curves. Such an approach allows to fit each mode of a waveform with the most suitable function and to deal with both, symmetric and asymmetric, echoes. The model takes into account a data term, which measures the coherence between the models and the waveforms, and a regularization term, which introduces prior knowledge on the reconstructed signal. The exploration of the associated configuration space is performed by a reversible jump Markov chain Monte Carlo (RJMCMC) sampler coupled with simulated annealing. Experiments with different kinds of lidar signals, especially from urban scenes, show the high potential of the proposed approach. To further demonstrate the advantages of the suggested method, actual laser scans are classified and the results are reported.
Sponsorship	IEEE Signal Processing Society
Page Count	18
File Size	2195721
Starting Page	3204
Ending Page	3221
File Format	PDF
ISSN	10577149
Volume Number	19
Issue Number	12
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-12-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Laser radar Image segmentation Laboratories Lattices Vectors Automation Clustering algorithms Multidimensional systems Object recognition Pattern recognition source modeling Lidar marked point process Monte Carlo sampling object-based stochastic model
Content Type	Text
Resource Type	Article
Subject	Computer Graphics and Computer-Aided Design Software

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