Seamless navigation and mapping using an INS/GNSS/grid-based SLAM semi-tightly coupled integration scheme

Chiang, Kai-Wei; Tsai, Guang-Je; Chang, Hao‐Wei; Joly, Cyril; EI-Sheimy, N.

doi:10.1016/j.inffus.2019.01.004

Cited by 64 publications

(19 citation statements)

References 24 publications

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“…In Equations (10)- (15), u n is the system input signals, X n is the state matrix of the system at the current time T, and the state parameters of the K 1 -K 4 Runge-Kutta algorithm. The main function of the Runge-Kutta algorithm is to update the system four quaternions, and finally calculate the attitude Euler angle of navigation system.…”

Section: Integrated Navigation System Mechanization Modelmentioning

confidence: 99%

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Indoor and Outdoor Low-Cost Seamless Integrated Navigation System Based on the Integration of INS/GNSS/LIDAR System

Guan

Gao

et al. 2020

Remote Sensing

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Global Navigation Satellite System (GNSS) provides accurate positioning data for vehicular navigation in open outdoor environment. In an indoor environment, Light Detection and Ranging (LIDAR) Simultaneous Localization and Mapping (SLAM) establishes a two-dimensional map and provides positioning data. However, LIDAR can only provide relative positioning data and it cannot directly provide the latitude and longitude of the current position. As a consequence, GNSS/Inertial Navigation System (INS) integrated navigation could be employed in outdoors, while the indoors part makes use of INS/LIDAR integrated navigation and the corresponding switching navigation will make the indoor and outdoor positioning consistent. In addition, when the vehicle enters the garage, the GNSS signal will be blurred for a while and then disappeared. Ambiguous GNSS satellite signals will lead to the continuous distortion or overall drift of the positioning trajectory in the indoor condition. Therefore, an INS/LIDAR seamless integrated navigation algorithm and a switching algorithm based on vehicle navigation system are designed. According to the experimental data, the positioning accuracy of the INS/LIDAR navigation algorithm in the simulated environmental experiment is 50% higher than that of the Dead Reckoning (DR) algorithm. Besides, the switching algorithm developed based on the INS/LIDAR integrated navigation algorithm can achieve 80% success rate in navigation mode switching.

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Section: Integrated Navigation System Mechanization Modelmentioning

confidence: 99%

“…In the research field of seamless navigation, the research of reference [15] is mainly based on the high-cost inertial navigation system and 16-line LIDAR. Due to the high positioning accuracy, the mode switching when the vehicle enters the garage will have little impact on the system positioning.…”

Section: Introductionmentioning

confidence: 99%

Indoor and Outdoor Low-Cost Seamless Integrated Navigation System Based on the Integration of INS/GNSS/LIDAR System

Guan

Gao

et al. 2020

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…where ωr is the rotation speed, and ϕ = ωr × t. For the sake of simplicity, we only consider the biases as the IMU errors and ignore the scale factor errors and misalignment errors of IMU. Take (1), (2) and (3) in (4) and (5) respectively, and we have:…”

Section: The Single-axis Rins Error Analysis With the Imu Biasesmentioning

confidence: 99%

“…Inertial Navigation System (INS), as an entirely self-contained system, can obtain attitude, velocity and position of the vehicle by resolving the data sampled by its Inertial Measurement Unit (IMU) containing tri-axis gyroscopes and accelerometers [1]. With the characteristics of high reliability and data rate, INS is widely used in airplanes, missiles, automobiles, submarines, ships, and robots [2][3][4][5]. However, the navigation errors of INS are mainly caused by the gyroscope drifts and accelerometer biases.…”

Section: Introductionmentioning

confidence: 99%

Thermal Modeling and Calibration Method in Complex Temperature Field for Single-Axis Rotational Inertial Navigation System

Wang

Cheng

Du³

2020

Sensors

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Single-axis rotational inertial navigation systems (single-axis RINSs) are widely used in high-accuracy navigation because of their ability to restrain the horizontal axis errors of the inertial measurement unit (IMU). The IMU errors, especially the biases, should be constant during each rotation cycle that is to be modulated and restrained. However, the temperature field, consisting of the environment temperature and the power heating of single-axis RINS, affects the IMU performance and changes the biases over time. To improve the precision of single-axis RINS, the change of IMU biases caused by the temperature should be calibrated accurately. The traditional thermal calibration model consists of the temperature and temperature change rate, which does not reflect the complex temperature field of single-axis RINS. This paper proposed a multiple regression method with a temperature gradient in the model, and in order to describe the complex temperature field thoroughly, a BP neural network method is proposed with consideration of the coupled items of the temperature variables. Experiments show that the proposed methods outperform the traditional calibration method. The navigation accuracy of single-axis RINS can be improved by up to 47.41% in lab conditions and 65.11% in the moving vehicle experiment, respectively.

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“…Thus, the related image processing algorithms [57], such as the camera calibration, the image enhancement, the image restoration, the image denoising, the feature detection, the feature matching, the image mosaic, and the image segmentation, can all be utilized. Recently, the Simultaneous Localization and Mapping (SLAM) technique [58] begins to be used for the map navigation in the narrow area. Its familiar methods include the Lidar-based SLAM and the visual-based SLAM.…”

Section: The Autonomous Navigation Algorithmsmentioning

confidence: 99%

Autonomous Navigation for Mars Exploration

Liu¹

2020

Mars Exploration - A Step Forward

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The autonomous navigation technology uses the multiple sensors to percept and estimate the spatial locations of the aerospace prober or the Mars rover and to guide their motions in the orbit or the Mars surface. In this chapter, the autonomous navigation methods for the Mars exploration are reviewed. First, the current development status of the autonomous navigation technology is summarized. The popular autonomous navigation methods, such as the inertial navigation, the celestial navigation, the visual navigation, and the integrated navigation, are introduced. Second, the application of the autonomous navigation technology for the Mars exploration is presented. The corresponding issues in the Entry Descent and Landing (EDL) phase and the Mars surface roving phase are mainly discussed. Third, some challenges and development trends of the autonomous navigation technology are also addressed.

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Seamless navigation and mapping using an INS/GNSS/grid-based SLAM semi-tightly coupled integration scheme

Cited by 64 publications

References 24 publications

Indoor and Outdoor Low-Cost Seamless Integrated Navigation System Based on the Integration of INS/GNSS/LIDAR System

Indoor and Outdoor Low-Cost Seamless Integrated Navigation System Based on the Integration of INS/GNSS/LIDAR System

Thermal Modeling and Calibration Method in Complex Temperature Field for Single-Axis Rotational Inertial Navigation System

Autonomous Navigation for Mars Exploration

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