An Improved Single-Epoch GNSS/INS Positioning Method for Urban Canyon Environment Based on Real-Time DISB Estimation

Ye, Fei; Pan, Shuguo; Gao, Wang; Wang, Hao; Liu, Guoliang; Wang, Yunfeng

doi:10.1109/access.2020.3044197

Cited by 7 publications

(2 citation statements)

References 36 publications

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“…Relocation is used to obtain the vehicle’s global position and pose based on the built map. Various relocation methods have been successful in resolving such problems using global navigation satellite systems (GNSS) [ 1 , 2 , 3 ], the inertial measurement unit (IMU) [ 4 , 5 ], cameras, LiDAR, and other sensing sensors.…”

Section: Introductionmentioning

confidence: 99%

A Method of Setting the LiDAR Field of View in NDT Relocation Based on ROI

Lan

Kong

et al. 2023

Sensors

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LiDAR placement and field of view selection play a role in detecting the relative position and pose of vehicles in relocation maps based on high-precision map automatic navigation. When the LiDAR field of view is obscured or the LiDAR position is misplaced, this can easily lead to loss of repositioning or low repositioning accuracy. In this paper, a method of LiDAR layout and field of view selection based on high-precision map normal distribution transformation (NDT) relocation is proposed to solve the problem of large NDT relocation error and position loss when the occlusion field of view is too large. To simulate the real placement environment and the LiDAR obstructed by obstacles, the ROI algorithm is used to cut LiDAR point clouds and to obtain LiDAR point cloud data of different sizes. The cut point cloud data is first downsampled and then relocated. The downsampling points for NDT relocation are recorded as valid matching points. The direction and angle settings of the LiDAR point cloud data are optimized using RMSE values and valid matching points. The results show that in the urban scene with complex road conditions, there are more front and rear matching points than left and right matching points within the unit angle. The more matching points of the NDT relocation algorithm there are, the higher the relocation accuracy. Increasing the front and rear LiDAR field of view prevents the loss of repositioning. The relocation accuracy can be improved by increasing the left and right LiDAR field of view.

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Section: Introductionmentioning

confidence: 99%

A Method of Setting the LiDAR Field of View in NDT Relocation Based on ROI

Lan

Kong

et al. 2023

Sensors

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show abstract

“…As unmanned vehicles and other unmanned areas of research become more and more popular [1], a variety of high-precision navigation and positioning methods are also rapidly developing, such as global navigation satellite systems (GNSS), inertial navigation systems (INS), vision navigation systems, geomagnetic navigation systems (MNS), bionic navigation systems, and microelectromechanical navigation systems (MEMS), etc. These are more widely used navigation methods, and the characteristics of each navigation system and the respective strengths and weaknesses of the mutual coexistence of the systems are examined in [2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Seamless MEMS-INS/Geomagnetic Navigation System Based on Deep-Learning Strong Tracking Square-Root Cubature Kalman Filter

Zhao,

Wang,

Shen

2023

Micromachines

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To suppress inertial navigation system drift and improve the seamless navigation capability of microelectromechanical system-inertial navigation systems/geomagnetic navigation systems (MEMS-INS/MNS) in geomagnetically unlocked environments, this paper proposes a hybrid seamless MEMS-INS/MNS strategy combining a strongly tracked square-root cubature Kalman filter with deep self-learning (DSL-STSRCKF). The proposed DSL-STSRCKF method consists of two innovative steps: (i) The relationship between the deep Kalman filter gain and the optimal estimation is established. In this paper, combining the two auxiliary methods of strong tracking filtering and square-root filtering based on singular value decomposition, the heading accuracy error of ST-SRCKF can reach 1.29°, which improves the heading accuracy by 90.10% and 9.20% compared to the traditional single INS and the traditional integrated navigation algorithm and greatly improves the robustness and computational efficiency. (ii) Providing deep self-learning capability for the ST-SRCKF by introducing a nonlinear autoregressive neural network (NARX) with exogenous inputs, which means that the heading accuracy can still reach 1.33° even during the MNS lockout period, and the heading accuracy can be improved by 89.80% compared with the single INS, realizing the continuous high-precision navigation estimation.

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Setting Method of Downsampling Factor and Grid Factor for NDT Relocation Algorithm in Dynamic Environment

Lan

Kong

et al. 2022

IEEE Access

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An Improved Single-Epoch GNSS/INS Positioning Method for Urban Canyon Environment Based on Real-Time DISB Estimation

Cited by 7 publications

References 36 publications

A Method of Setting the LiDAR Field of View in NDT Relocation Based on ROI

A Method of Setting the LiDAR Field of View in NDT Relocation Based on ROI

Seamless MEMS-INS/Geomagnetic Navigation System Based on Deep-Learning Strong Tracking Square-Root Cubature Kalman Filter

Setting Method of Downsampling Factor and Grid Factor for NDT Relocation Algorithm in Dynamic Environment

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