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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 Maximum Likelihood Value of IMU + LiDAR and LiDAR scan matching.
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sensors-15-16710-f008: The Maximum Likelihood Value of IMU + LiDAR and LiDAR scan matching.

Mentions: A series of for each navigation period with the IMU + LiDAR solution and LiDAR standalone solution are shown in Figure 8. The patterns of maximum are the same, which implies that the range estimation (and the displacement) is almost identical with two methods. We conclude that the difference in the final trajectories of the two methods is primarily affected by the heading estimation. The main error corrected by IMU is the attitude estimation, and Figure 9a,b show evidence that proves this result. After approximately 15 s, the cart enters the area of the small hall, where there is a relatively feature-poor environment. The heading estimated error appears with the LiDAR standalone solution and the accumulated error does not remain fixed to the end. At 60 s, the heading differences reach a maximum 3.7 degrees at the turn of the corridor, which is full of glass handrails. However, the results also prove that IMUs significantly contribute to attitude estimations, particularly for short-period heading estimations that can sustain an accurate heading estimation in a feature-poor environment for a short period until the LiDAR scan matching re-enters a feature rich environment.


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 Maximum Likelihood Value 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-f008: The Maximum Likelihood Value of IMU + LiDAR and LiDAR scan matching.
Mentions: A series of for each navigation period with the IMU + LiDAR solution and LiDAR standalone solution are shown in Figure 8. The patterns of maximum are the same, which implies that the range estimation (and the displacement) is almost identical with two methods. We conclude that the difference in the final trajectories of the two methods is primarily affected by the heading estimation. The main error corrected by IMU is the attitude estimation, and Figure 9a,b show evidence that proves this result. After approximately 15 s, the cart enters the area of the small hall, where there is a relatively feature-poor environment. The heading estimated error appears with the LiDAR standalone solution and the accumulated error does not remain fixed to the end. At 60 s, the heading differences reach a maximum 3.7 degrees at the turn of the corridor, which is full of glass handrails. However, the results also prove that IMUs significantly contribute to attitude estimations, particularly for short-period heading estimations that can sustain an accurate heading estimation in a feature-poor environment for a short period until the LiDAR scan matching re-enters a feature rich environment.

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.