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.


The Mapping results with IMU + LiDAR (blue dot) and LiDAR (black dot), compared with TLS (red dot).
© Copyright Policy
Related In: Results  -  Collection

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

sensors-15-16710-f007: The Mapping results with IMU + LiDAR (blue dot) and LiDAR (black dot), compared with TLS (red dot).

Mentions: The positioning results of the y-axis are better than the results of the x-axis, regardless of whether the IMU is integrated. Table 1 shows the numerical statistics of the stationary experiments. The RMS errors of the x-axis, y-axis, and heading estimation with the IMU + LiDAR solution are 0.009 m, 0.007 m, and 0.065°. However, the corresponding RMS errors with the LiDAR only solution are 0.007 m, 0.004 m, and 0.000°. The RMS error of the x-axis is higher than that of the y-axis because there are more features along the y-axis (along the corridor direction) than the x-axis (across the corridor direction) for scan matching. As shown in Figure 7a, almost all laser scan points are horizontally distributed; only a few points are vertically distributed, which makes the positioning accuracy of the Y direction greater than the X direction. This result proves that environmental features proportionally affect positioning results [19]. The reason that the heading estimation equals 0 is that the search step of the current IMLE is 0.25°, with a maximum detected range of 30 m; a 0.25° heading change will cause a maximum 5.3 cm displacement of the laser point on a 30 m target, and this circumstance never occurs during the stationary test.


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 Mapping results with IMU + LiDAR (blue dot) and LiDAR (black dot), compared with TLS (red dot).
© Copyright Policy
Related In: Results  -  Collection

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

sensors-15-16710-f007: The Mapping results with IMU + LiDAR (blue dot) and LiDAR (black dot), compared with TLS (red dot).
Mentions: The positioning results of the y-axis are better than the results of the x-axis, regardless of whether the IMU is integrated. Table 1 shows the numerical statistics of the stationary experiments. The RMS errors of the x-axis, y-axis, and heading estimation with the IMU + LiDAR solution are 0.009 m, 0.007 m, and 0.065°. However, the corresponding RMS errors with the LiDAR only solution are 0.007 m, 0.004 m, and 0.000°. The RMS error of the x-axis is higher than that of the y-axis because there are more features along the y-axis (along the corridor direction) than the x-axis (across the corridor direction) for scan matching. As shown in Figure 7a, almost all laser scan points are horizontally distributed; only a few points are vertically distributed, which makes the positioning accuracy of the Y direction greater than the X direction. This result proves that environmental features proportionally affect positioning results [19]. The reason that the heading estimation equals 0 is that the search step of the current IMLE is 0.25°, with a maximum detected range of 30 m; a 0.25° heading change will cause a maximum 5.3 cm displacement of the laser point on a 30 m target, and this circumstance never occurs during the stationary test.

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.