IEEE Trans Image Process - A marked point process for modeling lidar waveforms.

Tópicos

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Resumo

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 of parametric 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.

Resumo Limpo

lidar waveform d signal repres train echo caus reflect differ target model echo appropri parametr function use retriev inform physic characterist target paper present new probabilist model base upon mark point process reconstruct echo record discret waveform sequenc parametr curv approach allow fit mode waveform suitabl function deal symmetr asymmetr echo model take account data term measur coher model waveform regular term introduc prior knowledg reconstruct signal explor associ configur space perform revers jump markov chain mont carlo rjmcmc sampler coupl simul anneal experi differ kind lidar signal especi urban scene show high potenti propos approach demonstr advantag suggest method actual laser scan classifi result report

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