Next Article in Journal
Testing a Firefly-Inspired Synchronization Algorithm in a Complex Wireless Sensor Network
Next Article in Special Issue
An Improved Multi-Sensor Fusion Navigation Algorithm Based on the Factor Graph
Previous Article in Journal
Performance and Challenges of Service-Oriented Architecture for Wireless Sensor Networks
Previous Article in Special Issue
A Unified Model for BDS Wide Area and Local Area Augmentation Positioning Based on Raw Observations
Article Menu
Issue 3 (March) cover image

Export Article

Open AccessArticle
Sensors 2017, 17(3), 539; doi:10.3390/s17030539

LiDAR-IMU Time Delay Calibration Based on Iterative Closest Point and Iterated Sigma Point Kalman Filter

School of Mechanical and Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, China
Academic Editors: Cheng Wang, Julian Smit, Ayman F. Habib and Michael Ying Yang
Received: 3 January 2017 / Revised: 24 February 2017 / Accepted: 5 March 2017 / Published: 8 March 2017
(This article belongs to the Special Issue Multi-Sensor Integration and Fusion)
View Full-Text   |   Download PDF [4093 KB, uploaded 9 March 2017]   |  

Abstract

The time delay calibration between Light Detection and Ranging (LiDAR) and Inertial Measurement Units (IMUs) is an essential prerequisite for its applications. However, the correspondences between LiDAR and IMU measurements are usually unknown, and thus cannot be computed directly for the time delay calibration. In order to solve the problem of LiDAR-IMU time delay calibration, this paper presents a fusion method based on iterative closest point (ICP) and iterated sigma point Kalman filter (ISPKF), which combines the advantages of ICP and ISPKF. The ICP algorithm can precisely determine the unknown transformation between LiDAR-IMU; and the ISPKF algorithm can optimally estimate the time delay calibration parameters. First of all, the coordinate transformation from the LiDAR frame to the IMU frame is realized. Second, the measurement model and time delay error model of LiDAR and IMU are established. Third, the methodology of the ICP and ISPKF procedure is presented for LiDAR-IMU time delay calibration. Experimental results are presented that validate the proposed method and demonstrate the time delay error can be accurately calibrated. View Full-Text
Keywords: LiDAR; inertial measurement unit; iterative closest point; iterated sigma point Kalman filter; time delay calibration LiDAR; inertial measurement unit; iterative closest point; iterated sigma point Kalman filter; time delay calibration
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Liu, W. LiDAR-IMU Time Delay Calibration Based on Iterative Closest Point and Iterated Sigma Point Kalman Filter. Sensors 2017, 17, 539.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top