Authors:
Anas Alhashimi
;
Damiano Varagnolo
and
Thomas Gustafsson
Affiliation:
Luleå University of Technology, Sweden
Keyword(s):
Maximum Likelihood, Least Squares, Statistical Inference, Distance Mapping Sensors, Lidar, Nonlinear System, AIC.
Related
Ontology
Subjects/Areas/Topics:
Informatics in Control, Automation and Robotics
;
Nonlinear Signals and Systems
;
Sensors Fusion
;
Signal Processing, Sensors, Systems Modeling and Control
;
System Identification
;
System Modeling
Abstract:
We aim at developing statistical tools that improve the accuracy and precision of the measurements returned
by triangulation Light Detection and Rangings (Lidars). To this aim we: i) propose and validate a novel model
that describes the statistics of the measurements of these Lidars, and that is built starting from mechanical
considerations on the geometry and properties of their pinhole lens - CCD camera systems; ii) build, starting
from this novel statistical model, a Maximum Likelihood (ML) / Akaike Information Criterion (AIC) -
based sensor calibration algorithm that exploits training information collected in a controlled environment; iii)
develop ML and Least Squares (LS) strategies that use the calibration results to statistically process the raw
sensor measurements in non controlled environments. The overall technique allowed us to obtain empirical
improvements of the normalized Mean Squared Error (MSE) from 0.0789 to 0.0046.