LiDAR Scan Matching Aided Inertial Navigation System in GNSS-Denied Environments
A new scan that matches an aided Inertial Navigation System (INS) with a low-cost LiDAR is proposed as an alternative to GNSS-based navigation systems in GNSS-degraded or -denied environments such as indoor areas, dense forests, or urban canyons. In these areas, INS-based Dead Reckoning (DR) and Sim...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Online |
| Language: | English |
| Published: |
MDPI
2015
|
| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4541902/ |
| id |
pubmed-4541902 |
|---|---|
| recordtype |
oai_dc |
| spelling |
pubmed-45419022015-08-26 LiDAR Scan Matching Aided Inertial Navigation System in GNSS-Denied Environments Tang, Jian Chen, Yuwei Niu, Xiaoji Wang, Li Chen, Liang Liu, Jingbin Shi, Chuang Hyyppä, Juha Article A new scan that matches an aided Inertial Navigation System (INS) with a low-cost LiDAR is proposed as an alternative to GNSS-based navigation systems in GNSS-degraded or -denied environments such as indoor areas, dense forests, or urban canyons. In these areas, INS-based Dead Reckoning (DR) and Simultaneous Localization and Mapping (SLAM) technologies are normally used to estimate positions as separate tools. However, there are critical implementation problems with each standalone system. The drift errors of velocity, position, and heading angles in an INS will accumulate over time, and on-line calibration is a must for sustaining positioning accuracy. SLAM performance is poor in featureless environments where the matching errors can significantly increase. Each standalone positioning method cannot offer a sustainable navigation solution with acceptable accuracy. This paper integrates two complementary technologies—INS and LiDAR SLAM—into one navigation frame with a loosely coupled Extended Kalman Filter (EKF) to use the advantages and overcome the drawbacks of each system to establish a stable long-term navigation process. Static and dynamic field tests were carried out with a self-developed Unmanned Ground Vehicle (UGV) platform—NAVIS. The results prove that the proposed approach can provide positioning accuracy at the centimetre level for long-term operations, even in a featureless indoor environment. MDPI 2015-07-10 /pmc/articles/PMC4541902/ /pubmed/26184206 http://dx.doi.org/10.3390/s150716710 Text en © 2015 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/). |
| repository_type |
Open Access Journal |
| institution_category |
Foreign Institution |
| institution |
US National Center for Biotechnology Information |
| building |
NCBI PubMed |
| collection |
Online Access |
| language |
English |
| format |
Online |
| author |
Tang, Jian Chen, Yuwei Niu, Xiaoji Wang, Li Chen, Liang Liu, Jingbin Shi, Chuang Hyyppä, Juha |
| spellingShingle |
Tang, Jian Chen, Yuwei Niu, Xiaoji Wang, Li Chen, Liang Liu, Jingbin Shi, Chuang Hyyppä, Juha LiDAR Scan Matching Aided Inertial Navigation System in GNSS-Denied Environments |
| author_facet |
Tang, Jian Chen, Yuwei Niu, Xiaoji Wang, Li Chen, Liang Liu, Jingbin Shi, Chuang Hyyppä, Juha |
| author_sort |
Tang, Jian |
| title |
LiDAR Scan Matching Aided Inertial Navigation System in GNSS-Denied Environments |
| title_short |
LiDAR Scan Matching Aided Inertial Navigation System in GNSS-Denied Environments |
| title_full |
LiDAR Scan Matching Aided Inertial Navigation System in GNSS-Denied Environments |
| title_fullStr |
LiDAR Scan Matching Aided Inertial Navigation System in GNSS-Denied Environments |
| title_full_unstemmed |
LiDAR Scan Matching Aided Inertial Navigation System in GNSS-Denied Environments |
| title_sort |
lidar scan matching aided inertial navigation system in gnss-denied environments |
| description |
A new scan that matches an aided Inertial Navigation System (INS) with a low-cost LiDAR is proposed as an alternative to GNSS-based navigation systems in GNSS-degraded or -denied environments such as indoor areas, dense forests, or urban canyons. In these areas, INS-based Dead Reckoning (DR) and Simultaneous Localization and Mapping (SLAM) technologies are normally used to estimate positions as separate tools. However, there are critical implementation problems with each standalone system. The drift errors of velocity, position, and heading angles in an INS will accumulate over time, and on-line calibration is a must for sustaining positioning accuracy. SLAM performance is poor in featureless environments where the matching errors can significantly increase. Each standalone positioning method cannot offer a sustainable navigation solution with acceptable accuracy. This paper integrates two complementary technologies—INS and LiDAR SLAM—into one navigation frame with a loosely coupled Extended Kalman Filter (EKF) to use the advantages and overcome the drawbacks of each system to establish a stable long-term navigation process. Static and dynamic field tests were carried out with a self-developed Unmanned Ground Vehicle (UGV) platform—NAVIS. The results prove that the proposed approach can provide positioning accuracy at the centimetre level for long-term operations, even in a featureless indoor environment. |
| publisher |
MDPI |
| publishDate |
2015 |
| url |
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4541902/ |
| _version_ |
1613261505543798784 |