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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 field test cart platform.
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sensors-15-16710-f004: The field test cart platform.

Mentions: A series of field tests were performed to evaluate the proposed LiDAR-aided inertial navigation system based on [22]. As shown in Figure 4, an Xsens MTi-G IMU and a Hokuyo UTM-30LX-EW laser scanner were installed on a rigid platform and horizontally carried by a cart. They were connected to a laptop with a serial port and a USB port, respectively. The Xsens is a MEMs-based six Degree of Freedom (DOF) miniature commercial grade IMU with an output rate of 100 Hz, an Angular Random Walk (ARW) of 3 , a Velocity Random Walk (VRW) of 0.12 , and a Gyro and Accelerometer Bias Instability of 200 degree/h and 2000 mGal (1 Gal = 1 cm/s2) [6,31]; The coverage of the LiDAR sensor was approximately 0.1 m to 30 m with a 270° scan angle and an angular resolution of 0.25°. A software platform programmed with C++ and Qt was designed for recording the raw data and post-processing navigation; Figure 5 shows the Graphic User Interface (GUI) of the NAVIS software.


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 field test cart platform.
© Copyright Policy
Related In: Results  -  Collection

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

sensors-15-16710-f004: The field test cart platform.
Mentions: A series of field tests were performed to evaluate the proposed LiDAR-aided inertial navigation system based on [22]. As shown in Figure 4, an Xsens MTi-G IMU and a Hokuyo UTM-30LX-EW laser scanner were installed on a rigid platform and horizontally carried by a cart. They were connected to a laptop with a serial port and a USB port, respectively. The Xsens is a MEMs-based six Degree of Freedom (DOF) miniature commercial grade IMU with an output rate of 100 Hz, an Angular Random Walk (ARW) of 3 , a Velocity Random Walk (VRW) of 0.12 , and a Gyro and Accelerometer Bias Instability of 200 degree/h and 2000 mGal (1 Gal = 1 cm/s2) [6,31]; The coverage of the LiDAR sensor was approximately 0.1 m to 30 m with a 270° scan angle and an angular resolution of 0.25°. A software platform programmed with C++ and Qt was designed for recording the raw data and post-processing navigation; Figure 5 shows the Graphic User Interface (GUI) of the NAVIS software.

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.