Fiber optic gyro inertial navigation system (FOGINS), as a kind of navigation equipment with high accuracy, wide dynamic range, and simple mechanical structure, is widely used in moving platforms such as vehicles, aircraft, and ships. For some motion platforms, such as vehicles and aircraft, the FOGINS can be provided with a stationary state so as to realize the initial alignment under static base conditions. However, some motion platforms, such as ships and floating balloons, are always in a swaying state and cannot provide a stationary state for the FOGINS. In such special motion platforms, only the dynamic base initial alignment technique can be implemented. This paper proposes a moving base simulation method, through the simulation, and observed the mainstream initial algorithm effect under different conditions, and categorized to make the relevant analysis, provides a number of engineering guidance strategy, through these strategies, can effectively avoid huge errors of initial alignment, which affect the subsequent positioning accuracy.
KEYWORDS: Digital signal processing, Field programmable gate arrays, Data communications, Telecommunications, LCDs, Power grids, Control systems, Data transmission, Displays, Signal processing
In order to solve the problem that the single core DSP digital communication system can not meet the requirements of the complex APF(Active power filter) algorithm, a dual core digital communication system based on DSP and FPGA is proposed. The digital communication principle, chip selection and communication between each sub module of the system are described. On this basis, the software flow chart is designed, and an APF experimental platform based on DSP and FPGA is built to verify the feasibility of the scheme.
Cameras capture every pixel in transient duration for one frame image, while lidars capture every point in relevant long duration for one frame point cloud. When lidars stay static, point cloud will not cause motion distortion, when lidars move, point cloud will have motion distortion, this issue becomes worse with the increase of lidars’ linear velocity and angular velocity. As an inertial space movements’ sensor, IMU (inertial measurement unit) captures motions by a high frequency, which can be utilized to correct lidars motion distortion. In this paper, a variable motion model to correct lidars motion distortion is proposed. Comparing to the constant motion model, the proposed model has better performance on handling distortion caused by varying motion. Experiments results show that the variable motion model has a beneficial influence on distortion correction.
Vision inertial odometer (VIO) has been applied for SLAM (Simultaneous Localization and Mapping) this years, such device consists of camera and inertial measurement unit(IMU), and takes advantages of both sensors. When one of sensor’s quality is not as good as the other, we have to trust better one and implement this strategy in our algorithms. In this paper, influences of IMU’s quality on VIO are analyzed, based on ideal IMU’s data and multi-state constraint Kalman filter(MSCKF) method, different levels IMU are implemented in simulation, results show that: for high precision IMU, pure inertial navigation performance is better than VIO; when it comes to medium precision IMU, VIO performance are better if more features are tracked by camera. Further study shows that, the precision of MSCKF method can be improved by adjusting window size.
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