Optimal estimation of line segments in noisy lidar data

Lidar data usually is obtained by independently measuring distance r   and angle ϕϕ. Therefore, measurements of r   and ϕϕ 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.