Analysis of Multiple Positions for the Intrinsic and Extrinsic Calibration of a Multi-Beam LiDAR

verfasst von

Dominik Ernst, Sören Vogel, Hamza Alkhatib, Ingo Neumann

Abstract

The usage of light detection and ranging sensors (LiDARs) has grown rapidly in recent years. The ability to directly capture 3D point clouds is a big advantage compared to other visual systems like cameras. One disadvantage is that uncertainty information is difficult to obtain for these systems, although this information is crucial for the decisions based on the measurements. This becomes even more important, when LiDARs are used in conjunction with other sensors in multi-sensor systems (MSS). The sensor data fusion with different sensors requires an extrinsic calibration, which describes the transformation between the LiDAR frame and the body frame of the platform. This can be done utilizing object space information measured by the LiDAR, which is used to infer the origin of the sensor frame based on reference geometries. This process can be used to additionally determine intrinsic parameters of the sensor. Possible intrinsic parameters are corrections for the distance measurements or approximations for the uncertainty of the measurement elements. In this work, the determination of extrinsic and intrinsic parameters is combined for the first time with the approximation of a stochastic model for a multi-beam LiDAR. This is demonstrated on a real-data set of a Velodyne VLP-16, for which the transformation parameters between sensor frame and body frame are determined. Additionally, a distance offset is determined and the variance components are estimated to establish a better approximation for the stochastic model. The impact of the calibration field and choice of positions within this calibration field are shown and discussed. The results are evaluated in a separate experiment using a kinematic MSS.

Organisationseinheit(en)

Geodätisches Institut

Typ

Aufsatz in Konferenzband

Anzahl der Seiten

8

Publikationsdatum

09.08.2022

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Informationssysteme und -management, Information systems, Signalverarbeitung, Maschinelles Sehen und Mustererkennung

Elektronische Version(en)

https://doi.org/10.23919/FUSION49751.2022.9841366 (Zugang: Geschlossen)