Optimal estimation of line segments in noisy lidar data

Authors:
Andreas Kapp;Lutz Gröll
Affiliations:
University of Karlsruhe (TH), Institute of Measurement and Control, Karlsruhe, Germany;Forschungszentrum Karlsruhe, Institute for Applied Computer Science, Karlsruhe, Germany
Venue:
Signal Processing - Signal processing in UWB communications
Year:
2006

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Abstract

Lidar data usually is obtained by independently measuring distance r and angle ϕ. Therefore, measurements of r and ϕ are statistically independent. However, in most approaches measurements in x and y are assumed to be uncorrelated thus not taking properly into account the noise characteristic.This article investigates the application of least squares (LS), total least squares (TLS), mixed-LS-TLS (MTLS), structured total least norm (STLN) and maximum-likelihood (ML) estimators to the problem of estimating line segments in noisy lidar data and compares their performance from a theoretical point of view. This analysis is supported by simulation results. A new approach of estimating an arbitrary line segment without the need of parametric constraints is proposed.