Limits...
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.


An example of IMLE scan-matching algorithm.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4541902&req=5

sensors-15-16710-f002: An example of IMLE scan-matching algorithm.

Mentions: Based on the above model, a brute search algorithm can be deployed to estimate the best body transformation within the entire map M. However, this is a time-consuming process; a more practical approach is to search in a refined local search scope extrapolated from the previous state, which can be obtained from the INS. Figure 2 shows an example of an IMLE scan matching algorithm. The red rectangle points indicate the current scan, which searches in the background likelihood map to determine the optimum position and attitude with maximum likelihood values.


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)

An example of IMLE scan-matching algorithm.
© Copyright Policy
Related In: Results  -  Collection

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

sensors-15-16710-f002: An example of IMLE scan-matching algorithm.
Mentions: Based on the above model, a brute search algorithm can be deployed to estimate the best body transformation within the entire map M. However, this is a time-consuming process; a more practical approach is to search in a refined local search scope extrapolated from the previous state, which can be obtained from the INS. Figure 2 shows an example of an IMLE scan matching algorithm. The red rectangle points indicate the current scan, which searches in the background likelihood map to determine the optimum position and attitude with maximum likelihood values.

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.