A Least Squares Collocation Method for Accuracy Improvement of Mobile LiDAR Systems

Mao, Qingzhou; Zhang, Liang; Li, Qingquan; Hu, Qingwu; Yu, Jingfei; Feng, Shaojun; Ochieng, Washington Yotto; Gong, Hanlu

doi:10.3390/rs70607402

Cited by 19 publications

(16 citation statements)

References 14 publications

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“…where θ α and ψ α are the installation error of pitch and heading angles of the odometer coordinate system relative to the INS coordinate system, respectively, and are usually added to state variables in the GNSS/INS-integrated navigation process in order for them to be solved together; however, there are no GNSS signals in a subway environment. In [32] and [33], the coordinates of control points in a The propagation equation of position error δp calculated by INS is:…”

Section: The Ins/odometer Integrated Navigation Algorithmmentioning

confidence: 99%

“…α θ , α ψ , and δK D are usually added to state variables in the GNSS/INS-integrated navigation process in order for them to be solved together; however, there are no GNSS signals in a subway environment. In [32] and [33], the coordinates of control points in a subway environment are used as external reference information to estimate the parameters α θ , α ψ , and δK D . However, the number of control points is small and the distance interval is 60 m, which provides too little information to estimate that many unknown parameters with good accuracy.…”

Section: The Ins/odometer Integrated Navigation Algorithmmentioning

confidence: 99%

“…Li Q. used stereo cameras to connect a track control network with MLSS [32], and Mao Q., Zhang L., and Li Q. et al utilized a laser scanner to transfer the coordinates of railway track control points to MLSS [33]. They used the coordinates of railway track control points to correct the accumulated errors in order to improve efficiency and accuracy [34].…”

Section: Introductionmentioning

confidence: 99%

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Absolute Positioning and Orientation of MLSS in a Subway Tunnel Based on Sparse Point-Assisted DR

Wang

Tang

Dong

et al. 2020

Sensors

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When performing the inspection of subway tunnels, there is an immense amount of data to be collected and the time available for inspection is short; however, the requirement for inspection accuracy is high. In this study, a mobile laser scanning system (MLSS) was used for the inspection of subway tunnels, and the key technology of the positioning and orientation system (POS) was investigated. We utilized the inertial measurement unit (IMU) and the odometer as the core sensors of the POS. The initial attitude of the MLSS was obtained by using a static initial alignment method. Considering that there is no global navigation satellite system (GNSS) signal in a subway, the forward and backward dead reckoning (DR) algorithm was used to calculate the positions and attitudes of the MLSS from any starting point in two directions. While the MLSS passed by the control points distributed on both sides of the track, the local coordinates of the control points were transmitted to the center of the MLSS by using the ranging information of the laser scanner. Then, a four-parameter transformation method was used to correct the error of the POS and transform the 3-D state information of the MLSS from a navigation coordinate system (NCS) to a local coordinate system (LCS). This method can completely eliminate a MLSS’s dependence on GNSS signals, and the obtained positioning and attitude information can be used for point cloud data fusion to directly obtain the coordinates in the LCS. In a tunnel of the Beijing–Zhangjiakou high-speed railway, when the distance interval of the control points used for correction was 120 m, the accuracy of the 3-D coordinates of the point clouds was 8 mm, and the experiment also showed that it takes less than 4 h to complete all the inspection work for a 5–6 km long tunnel. Further, the results from the inspection work of Wuhan subway lines showed that when the distance intervals of the control points used for correction were 60 m, 120 m, 240 m, and 480 m, the accuracies of the 3-D coordinates of the point clouds in the local coordinate system were 4 mm, 6 mm, 7 mm, and 8 mm, respectively.

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Section: The Ins/odometer Integrated Navigation Algorithmmentioning

confidence: 99%

Section: The Ins/odometer Integrated Navigation Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Absolute Positioning and Orientation of MLSS in a Subway Tunnel Based on Sparse Point-Assisted DR

Wang

Tang

Dong

et al. 2020

Sensors

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“…Secondly, the cost of the traditional MMS including high-precision GNSS/INS system is usually more than 300,000 dollars. Thirdly, the mapping results need plenty of post-processing work (Mao et al, 2015;Yu et al, 2015). Therefore, it is necessary to develop a low-cost solution do not rely on the GNSS in indoor and outdoor scenes.…”

Section: Introductionmentioning

confidence: 99%

Low-Cost Wheeled Robot-Borne Laser Scanning System for Indoor and Outdoor 3d Mapping Application

Chen

Cong

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> Aiming to accomplish automatic and real-time three-dimensional mapping in both indoor and outdoor scenes, a low-cost wheeled robot-borne laser scanning system is proposed in this paper. The system includes a laser scanner, an inertial measurement unit, a modified turtlebot3 two-wheel differential chassis and etc. To achieve a globally consistent map, the system performs global trajectory optimization after detecting the loop closure. Experiments are undertaken in two typical indoor/outdoor scenes that is an underground car park and a road environment in the campus of Wuhan University. The point clouds acquired by the proposed system are quantitatively evaluated by comparing the derived point clouds with the ground truth data collected by a RIEGL VZ 400 laser scanner. The results present an accuracy of 90% points below 0.1 meter error in the tested scene, showing that its applicability and potential in indoor and mapping applications.</p>

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“…The LSC algorithm has been used to estimate crustal movement signals from GPS velocity fields [9][10][11][12][13][14][15]. It has also been used in various earth science fields to control systematic and anomaly errors in GIS spatial data [16], detect outliers in multibeam bathymetric data [17], solve common point coordinate errors in 3D coordinate transformation [18], improve the accuracy of mobile light detection and ranging (LiDAR) systems in hostile environments [19], improve the results of downward continuation values of airborne gravity data [20], and refine the local covariance model of gravity anomalies [21].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Least-Squares Collocation Algorithm Considering Distance Scale Factor for GPS Crustal Velocity Field Fitting and Estimation

Chen

Liang

et al. 2019

Remote Sensing

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High-precision, high-reliability, and high-density GPS crustal velocity are extremely important requirements for geodynamic analysis. The least-squares collocation algorithm (LSC) has unique advantages over crustal movement models to overcome observation errors in GPS data and the sparseness and poor geometric distribution in GPS observations. However, traditional LSC algorithms often encounter negative covariance statistics, and thus, calculating statistical Gaussian covariance function based on the selected distance interval leads to inaccurate estimation of the correlation between the random signals. An unreliable Gaussian statistical covariance function also leads to inconsistency in observation noise and signal variance. In this study, we present an improved LSC algorithm that takes into account the combination of distance scale factor and adaptive adjustment to overcome these problems. The rationality and practicability of the new algorithm was verified by using GPS observations. Results show that the new algorithm introduces the distance scale factor, which effectively weakens the influence of systematic errors by improving the function model. The new algorithm can better reflect the characteristics of GPS crustal movement, which can provide valuable basic data for use in the analysis of regional tectonic dynamics using GPS observations.

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A Least Squares Collocation Method for Accuracy Improvement of Mobile LiDAR Systems

Cited by 19 publications

References 14 publications

Absolute Positioning and Orientation of MLSS in a Subway Tunnel Based on Sparse Point-Assisted DR

Absolute Positioning and Orientation of MLSS in a Subway Tunnel Based on Sparse Point-Assisted DR

Low-Cost Wheeled Robot-Borne Laser Scanning System for Indoor and Outdoor 3d Mapping Application

Adaptive Least-Squares Collocation Algorithm Considering Distance Scale Factor for GPS Crustal Velocity Field Fitting and Estimation

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