Strapdown Inertial Navigation Integration Algorithm Design Part 2: Velocity and Position Algorithms

Savage, Paul G.

doi:10.2514/2.4242

Cited by 334 publications

(205 citation statements)

References 6 publications

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“…The model is commonly used in estimation with IMUs originating from (Savage, 1998) using similar simplifications for MEMS-IMUs as in (Shin and El-Sheimy, 2004). 1) Nonlinear Model: Under the assumption that the measured effects of the Earth's rotation are small compared to the gyroscope accuracy, we can write the IMU kinematics combined with simple dynamic bias models as:…”

Section: B Imu Kinematics and Bias Modelmentioning

confidence: 99%

Keyframe-based visual–inertial odometry using nonlinear optimization

Leutenegger

Lynen

Bosse

et al. 2014

The International Journal of Robotics Research

1,501

1,009

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Combining visual and inertial measurements has become popular in mobile robotics, since the two sensing modalities offer complementary characteristics that make them the ideal choice for accurate Visual-Inertial Odometry or Simultaneous Localization and Mapping (SLAM). While historically the problem has been addressed with filtering, advancements in visual estimation suggest that non-linear optimization offers superior accuracy, while still tractable in complexity thanks to the sparsity of the underlying problem. Taking inspiration from these findings, we formulate a rigorously probabilistic cost function that combines reprojection errors of landmarks and inertial terms. The problem is kept tractable and thus ensuring real-time operation by limiting the optimization to a bounded window of keyframes through marginalization. Keyframes may be spaced in time by arbitrary intervals, while still related by linearized inertial terms. We present evaluation results on complementary datasets recorded with our custom-built stereo visual-inertial hardware that accurately synchronizes accelerometer and gyroscope measurements with imagery. A comparison of both a stereo and monocular version of our algorithm with and without online extrinsics estimation is shown with respect to ground truth. Furthermore, we compare the performance to an implementation of a state-of-the-art stochasic cloning sliding-window filter. This competititve reference implementation performs tightly-coupled filtering-based visual-inertial odometry. While our approach declaredly demands more computation, we show its superior performance in terms of accuracy.

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Section: B Imu Kinematics and Bias Modelmentioning

confidence: 99%

Keyframe-based visual–inertial odometry using nonlinear optimization

Leutenegger

Lynen

Bosse

et al. 2014

The International Journal of Robotics Research

1,501

1,009

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show abstract

“…After the system initialization, the compensated raw IMU outputs are used in the INS mechanization to provide high data-rate position, velocity and attitude (PVA) information. In the process of INS mechanization, the second-order coning correction term, the rotational and sculling motion effect are considered to weaken their influences on the attitude and velocity update [40,41].…”

Section: Overview Of Multi-gnss Rtk/ins Tightly-coupled Integration Wmentioning

confidence: 99%

High-Accuracy Positioning in Urban Environments Using Single-Frequency Multi-GNSS RTK/MEMS-IMU Integration

et al. 2018

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Abstract:The integration of Global Positioning System (GPS) real-time kinematics (RTK) and an inertial navigation system (INS) has been widely used in many applications, such as mobile mapping and autonomous vehicle control. Such applications require high-accuracy position information. However, continuous and reliable high-accuracy positioning is still challenging for GPS/INS integration in urban environments because of the limited satellite visibility, increasing multipath, and frequent signal blockages. Recently, with the rapid deployment of multi-constellation Global Navigation Satellite System (multi-GNSS) and the great advances in low-cost micro-electro-mechanical-system (MEMS) inertial measurement units (IMUs), it is expected that the positioning performance could be improved significantly. In this contribution, the tightly-coupled single-frequency multi-GNSS RTK/MEMS-IMU integration is developed to provide precise and continuous positioning solutions in urban environments. The innovation-based outlier-resistant ambiguity resolution (AR) and Kalman filtering strategy are proposed specifically for the integrated system to resist the measurement outliers or poor-quality observations. A field vehicular experiment was conducted in Wuhan City to evaluate the performance of the proposed algorithm. Results indicate that it is feasible for the proposed algorithm to obtain high-accuracy positioning solutions in the presence of measurement outliers. Moreover, the tightly-coupled single-frequency multi-GNSS RTK/MEMS-IMU integration even outperforms the dual-frequency multi-GNSS RTK in terms of AR and positioning performance for short baselines in urban environments.

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“…Then, accelerometer signals are transformed to the reference navigation frame using calculated attitude parameters. Finally, position and velocity can be determined by integrating the transformed accelerations (Savage 1998b). Therefore, the INS equation can be used as the process model to transform the previous state to the current state.…”

Section: Nonlinear Process Modelmentioning

confidence: 99%

Pose Estimation of Unmanned Aerial Vehicles Based on a Vision-Aided Multi-Sensor Fusion

Abdi¹,

Samadzadegan²,

Kurz³

2016

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

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ABSTRACT:GNSS/IMU navigation systems offer low-cost and robust solution to navigate UAVs. Since redundant measurements greatly improve the reliability of navigation systems, extensive researches have been made to enhance the efficiency and robustness of GNSS/IMU by additional sensors. This paper presents a method for integrating reference data, images taken from UAVs, barometric height data and GNSS/IMU data to estimate accurate and reliable pose parameters of UAVs. We provide improved pose estimations by integrating multi-sensor observations in an EKF algorithm with IMU motion model. The implemented methodology has demonstrated to be very efficient and reliable for automatic pose estimation. The calculated position and attitude of the UAV especially when we removed the GNSS from the working cycle clearly indicate the ability of the purposed methodology.

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Strapdown Inertial Navigation Integration Algorithm Design Part 2: Velocity and Position Algorithms

Cited by 334 publications

References 6 publications

Keyframe-based visual–inertial odometry using nonlinear optimization

Keyframe-based visual–inertial odometry using nonlinear optimization

High-Accuracy Positioning in Urban Environments Using Single-Frequency Multi-GNSS RTK/MEMS-IMU Integration

Pose Estimation of Unmanned Aerial Vehicles Based on a Vision-Aided Multi-Sensor Fusion

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