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LiDAR Scan Matching Aided Inertial Navigation System in GNSS-Denied Environments.

Tang J, Chen Y, Niu X, Wang L, Chen L, Liu J, Shi C, Hyyppä J - Sensors (Basel) (2015)

Bottom Line: SLAM performance is poor in featureless environments where the matching errors can significantly increase.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.

View Article: PubMed Central - PubMed

Affiliation: GNSS Research Center, Wuhan University, 129 Luoyu Road, Wuhan 430079, Hubei, China. tangjian@whu.edu.cn.

ABSTRACT
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.

No MeSH data available.


(a) Likelihood map result of static filed test. (b) The positioning result plot with IMU + LiDAR; (c) The positioning result plot with LiDAR scan matching; (d) The heading result of IMU + LiDAR and LiDAR scan matching.
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sensors-15-16710-f006: (a) Likelihood map result of static filed test. (b) The positioning result plot with IMU + LiDAR; (c) The positioning result plot with LiDAR scan matching; (d) The heading result of IMU + LiDAR and LiDAR scan matching.

Mentions: The stationary positioning experiment was performed at the beginning of the corridor for approximately 3 min. The NAVIS was installed on a cart at an installation height of approximately 1.3 m. As seen in Figure 6a, the likelihood map result of the corridor shows a featureless environment where straight parallel walls dominate the scene; Figures 6b,c show the positioning results of the IMU + LiDAR and LiDAR scan matching, respectively. Figure 6d shows the compared heading result with the two different methods. The result plots provide confirmatory evidence for the following conclusions:


LiDAR Scan Matching Aided Inertial Navigation System in GNSS-Denied Environments.

Tang J, Chen Y, Niu X, Wang L, Chen L, Liu J, Shi C, Hyyppä J - Sensors (Basel) (2015)

(a) Likelihood map result of static filed test. (b) The positioning result plot with IMU + LiDAR; (c) The positioning result plot with LiDAR scan matching; (d) The heading result of IMU + LiDAR and LiDAR scan matching.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4541902&req=5

sensors-15-16710-f006: (a) Likelihood map result of static filed test. (b) The positioning result plot with IMU + LiDAR; (c) The positioning result plot with LiDAR scan matching; (d) The heading result of IMU + LiDAR and LiDAR scan matching.
Mentions: The stationary positioning experiment was performed at the beginning of the corridor for approximately 3 min. The NAVIS was installed on a cart at an installation height of approximately 1.3 m. As seen in Figure 6a, the likelihood map result of the corridor shows a featureless environment where straight parallel walls dominate the scene; Figures 6b,c show the positioning results of the IMU + LiDAR and LiDAR scan matching, respectively. Figure 6d shows the compared heading result with the two different methods. The result plots provide confirmatory evidence for the following conclusions:

Bottom Line: SLAM performance is poor in featureless environments where the matching errors can significantly increase.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.

View Article: PubMed Central - PubMed

Affiliation: GNSS Research Center, Wuhan University, 129 Luoyu Road, Wuhan 430079, Hubei, China. tangjian@whu.edu.cn.

ABSTRACT
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.

No MeSH data available.