A Density-Based Data Reduction Algorithm for Robust Estimators

In this paper we present a non parametric density-based data reduction technique designed to be used in robust parameter estimation problems. Existing approaches are focused on reducing the amount of data preserving the density function. In our case the reduction is oriented to automatically remove the samples that are considered non interesting while taking into account those that are meaningful, those that have a high density associated. We use this filtering process to simplify the data sets in order to improve the performance of robust parameter estimators. We show its results when used along an existing estimator on synthetic and real LADAR data.