Correcting NLOS by 3D LiDAR and building height to improve GNSS single point positioning

Wen, Weisong; Zhang, Guohao; Hsu, Li‐Ta

doi:10.1002/navi.335

Cited by 59 publications

(45 citation statements)

References 33 publications

(38 reference statements)

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“…Compared with the optical scanning system, the 3D LiDAR sensor is more mature in terms of application in autonomous driving vehicles. In our team's previous work [34,35], we employ the 3D LiDAR and building height to correct the NLOS receptions. Improved GNSS positioning was obtained with the NLOS correction method.…”

Section: Introductionmentioning

confidence: 99%

Using Sky‐pointing fish‐eye camera and LiDAR to aid GNSS single‐point positioning in urban canyons

Bai

Wen

Hsu

2020

IET Intelligent Transport Systems

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Section: Introductionmentioning

confidence: 99%

Using Sky‐pointing fish‐eye camera and LiDAR to aid GNSS single‐point positioning in urban canyons

Bai

Wen

Hsu

2020

IET Intelligent Transport Systems

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“…In the future, the absolute positioning from the Global Navigation Satellite System (GNSS) real-time kinematic positioning (RTK) will be integrated with the proposed LC-LIO pipeline to achieve the drift-free motion estimation and map. Moreover, according to recent findings [43][44][45], the generated map of the surrounding area can effectively detect the GPS outlier measurements in urban canyons. Therefore, it deserves to make an exploration on the potential of the proposed LC-LIO pipeline in improving the GPS positioning.…”

Section: Conclusion and Future Perspectivesmentioning

confidence: 91%

Coarse-to-Fine Loosely-Coupled LiDAR-Inertial Odometry for Urban Positioning and Mapping

et al. 2021

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Accurate positioning and mapping are significant for autonomous systems with navigation requirements. In this paper, a coarse-to-fine loosely-coupled (LC) LiDAR-inertial odometry (LC-LIO) that could explore the complementariness of LiDAR and inertial measurement unit (IMU) was proposed for the real-time and accurate pose estimation of a ground vehicle in urban environments. Different from the existing tightly-coupled (TC) LiDAR-inertial fusion schemes which directly use all the considered ranges and inertial measurements to optimize the vehicle pose, the method proposed in this paper performs loosely-couped integrated optimization with the high-frequency motion prediction, which was produced by IMU integration based on optimized results, employed as the initial guess of LiDAR odometry to approach the optimality of LiDAR scan-to-map registration. As one of the prominent contributions, thorough studies were conducted on the performance upper bound of the TC LiDAR-inertial fusion schemes and LC ones, respectively. Furthermore, the experimental verification was performed on the proposition that the proposed pipeline can fully relax the potential of the LiDAR measurements (centimeter-level ranging accuracy) in a coarse-to-fine way without being disturbed by the unexpected IMU bias. Moreover, an adaptive covariance estimation method employed during LC optimization was proposed to explain the uncertainty of LiDAR scan-to-map registration in dynamic scenarios. Furthermore, the effectiveness of the proposed system was validated on challenging real-world datasets. Meanwhile, the process that the proposed pipelines realized the coarse-to-fine LiDAR scan-to-map registration was presented in detail. Comparing with the existing state-of-the-art TC-LIO, the focus of this study would be placed on that the proposed LC-LIO work could achieve similar or better accuracy with a reduced computational expense.

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“…If an NLOS signal is rejected in an urban environment, problems such as an insufficient number of satellites and a deterioration of the satellite geometry will occur. A few methods for correcting NLOS multipath errors using 3D maps have been proposed [ 21 , 22 ]. However, the correction of NLOS multipath errors is extremely complex and difficult and is not yet practical in terms of accuracy.…”

Section: Introductionmentioning

confidence: 99%

NLOS Multipath Classification of GNSS Signal Correlation Output Using Machine Learning

Suzuki

Amano

2021

Sensors

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This paper proposes a method for detecting non-line-of-sight (NLOS) multipath, which causes large positioning errors in a global navigation satellite system (GNSS). We use GNSS signal correlation output, which is the most primitive GNSS signal processing output, to detect NLOS multipath based on machine learning. The shape of the multi-correlator outputs is distorted due to the NLOS multipath. The features of the shape of the multi-correlator are used to discriminate the NLOS multipath. We implement two supervised learning methods, a support vector machine (SVM) and a neural network (NN), and compare their performance. In addition, we also propose an automated method of collecting training data for LOS and NLOS signals of machine learning. The evaluation of the proposed NLOS detection method in an urban environment confirmed that NN was better than SVM, and 97.7% of NLOS signals were correctly discriminated.

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Correcting NLOS by 3D LiDAR and building height to improve GNSS single point positioning

Cited by 59 publications

References 33 publications

Using Sky‐pointing fish‐eye camera and LiDAR to aid GNSS single‐point positioning in urban canyons

Using Sky‐pointing fish‐eye camera and LiDAR to aid GNSS single‐point positioning in urban canyons

Coarse-to-Fine Loosely-Coupled LiDAR-Inertial Odometry for Urban Positioning and Mapping

NLOS Multipath Classification of GNSS Signal Correlation Output Using Machine Learning

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