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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.


The system architecture of the LiDAR-aided Inertial Navigation System.
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sensors-15-16710-f003: The system architecture of the LiDAR-aided Inertial Navigation System.

Mentions: An Extended Kalman Filter is selected to fuse the measurements of the INS and LiDAR scan matching; an overview of the system architecture is shown in Figure 3. The Kalman filter algorithm involves predicting the state based on the system model and updating the state based on the measurements [29,30]. However, the output frequency of an IMU is higher than LiDAR measuring. For example, the output rate of an Xsens MTi IMU is approximately 100 Hz, whereas the adopted Hokuyo LiDAR measuring rate is only 40 Hz. Thus, the IMU predicts the state at every period by mechanization; EKF filters the results only when the periods of LiDAR observation information are obtained. The state error corrections are then estimated and fed back to the IMU mechanization for estimating the final navigation state , which will be the initial state for the LiDAR scan-matching search at the next period. The next filter iteration then continues.


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)

The system architecture of the LiDAR-aided Inertial Navigation System.
© Copyright Policy
Related In: Results  -  Collection

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

sensors-15-16710-f003: The system architecture of the LiDAR-aided Inertial Navigation System.
Mentions: An Extended Kalman Filter is selected to fuse the measurements of the INS and LiDAR scan matching; an overview of the system architecture is shown in Figure 3. The Kalman filter algorithm involves predicting the state based on the system model and updating the state based on the measurements [29,30]. However, the output frequency of an IMU is higher than LiDAR measuring. For example, the output rate of an Xsens MTi IMU is approximately 100 Hz, whereas the adopted Hokuyo LiDAR measuring rate is only 40 Hz. Thus, the IMU predicts the state at every period by mechanization; EKF filters the results only when the periods of LiDAR observation information are obtained. The state error corrections are then estimated and fed back to the IMU mechanization for estimating the final navigation state , which will be the initial state for the LiDAR scan-matching search at the next period. The next filter iteration then continues.

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.