scholarly journals State Observability through Prior Knowledge: Analysis of the Height Map Prior for Track Cycling

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2438 ◽  
Author(s):  
Tom L. Koller ◽  
Udo Frese
Keyword(s):  
Prior Knowledge ◽  
Pose Estimation ◽  
Rank Test ◽  
Navigation Systems ◽  
Inertial Navigation Systems ◽  
Knowledge Analysis ◽  
Race Track ◽  
Position And Orientation ◽  
State Observability ◽  
Track Cycling

Inertial navigation systems suffer from unbounded errors in the position and orientation estimates. This drift can be corrected by applying prior knowledge, instead of using exteroceptive sensors. We want to show that the use of prior knowledge can yield full observability of the position and orientation. A previous study showed that track cyclers can be tracked drift-free with an IMU as the only sensor and the knowledge that the bike drives on the track. In this paper, we analyze the observability of the pose in the experiment we conducted. Furthermore, we improve the pose estimation of the previous study. The observability is analyzed by testing the weak observability criterion with a Jacobian rank test. The improved estimator is presented and evaluated on a dataset with three 60-round trials (10 km each). The average RMS is 1.08 m and the estimate is drift-free. The observability analysis reveals that the system can gain complete observability in the curves and observability of the orientation on the straight parts of the race track.

scholarly journals A Dynamic Calibration Method of Installation Misalignment Angles between Two Inertial Navigation Systems

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2947
Author(s):  
Ming Hua ◽  
Kui Li ◽  
Yanhong Lv ◽  
Qi Wu
Keyword(s):  
Autonomous Navigation ◽  
Inertial Navigation ◽  
Calibration Method ◽  
Measurement Information ◽  
Dynamic Calibration ◽  
Navigation Systems ◽  
Inertial Navigation Systems ◽  
Additional Information ◽  
Two Systems ◽  
Dynamic Calibration Method

Generally, in order to ensure the reliability of Navigation system, vehicles are usually equipped with two or more sets of inertial navigation systems (INSs). Fusion of navigation measurement information from different sets of INSs can improve the accuracy of autonomous navigation effectively. However, due to the existence of misalignment angles, the coordinate axes of different systems are usually not in coincidence with each other absolutely, which would lead to serious problems when integrating the attitudes information. Therefore, it is necessary to precisely calibrate and compensate the misalignment angles between different systems. In this paper, a dynamic calibration method of misalignment angles between two systems was proposed. This method uses the speed and attitude information of two sets of INSs during the movement of the vehicle as measurements to dynamically calibrate the misalignment angles of two systems without additional information sources or other external measuring equipment, such as turntable. A mathematical model of misalignment angles between two INSs was established. The simulation experiment and the INSs vehicle experiments were conducted to verify the effectiveness of the method. The results show that the calibration accuracy of misalignment angles between the two sets of systems can reach to 1″ while using the proposed method.

scholarly journals Camera and inertial sensor fusion for the PnP problem: algorithms and experimental results

2021 ◽  
Vol 32 (4) ◽  
Author(s):  
Luigi D’Alfonso ◽  
Emanuele Garone ◽  
Pietro Muraca ◽  
Paolo Pugliese
Keyword(s):  
Pose Estimation ◽  
Good Accuracy ◽  
Inertial Sensor ◽  
Experimental Tests ◽  
Inertial Measurement Units ◽  
Magnetic Vector ◽  
Inertial Measurement ◽  
The Earth ◽  
Measurement Units ◽  
Position And Orientation

AbstractIn this work, we face the problem of estimating the relative position and orientation of a camera and an object, when they are both equipped with inertial measurement units (IMUs), and the object exhibits a set of n landmark points with known coordinates (the so-called Pose estimation or PnP Problem). We present two algorithms that, fusing the information provided by the camera and the IMUs, solve the PnP problem with good accuracy. These algorithms only use the measurements given by IMUs’ inclinometers, as the magnetometers usually give inaccurate estimates of the Earth magnetic vector. The effectiveness of the proposed methods is assessed by numerical simulations and experimental tests. The results of the tests are compared with the most recent methods proposed in the literature.

Analysis of Three Velocity Plus Attitude Matching Methods for Transfer Alignment

2012 ◽  
Vol 433-440 ◽  
pp. 2802-2807
Author(s):  
Ying Hong Han ◽  
Wan Chun Chen
Keyword(s):  
Kalman Filter ◽  
Inertial Navigation ◽  
Navigation Systems ◽  
Misalignment Angle ◽  
Inertial Navigation Systems ◽  
Excellent Performance ◽  
Transfer Alignment ◽  
Matching Methods ◽  
Moving Base

For inertial navigation systems (INS) on moving base, transfer alignment is widely applied to initialize it. Three velocity plus attitude matching methods are compared. And Kalman filter is employed to evaluate the misalignment angle. Simulations under the same conditions show which scheme has excellent performance in precision and rapidness of estimations.

INS/GNSS Integration with Account for Timing Skew and Displacement of GNSS Antenna. Experience of Practical Realization

2021 ◽  
Vol 29 (3) ◽  
pp. 52-68
Author(s):  
N.B. Vavilova ◽  
◽  
A.A. Golovan ◽  
A.V. Kozlov ◽  
I.A. Papusha ◽  
...  
Keyword(s):  
Spatial Separation ◽  
Measurement Unit ◽  
Complex Data ◽  
Navigation Systems ◽  
Inertial Navigation Systems ◽  
Positioning Systems ◽  
Global Positioning ◽  
Antenna Phase ◽  
Antenna Phase Center ◽  
Timing Skew

We examine two aspects specific to complex data fusion algorithms in integrated strapdown inertial navigation systems aided by global positioning systems, with their inherent spatial separation between the GNSS antenna phase center and the inertial measurement unit, as well as with the timing skew between their measurements. The first aspect refers to modifications of mathematical models used in INS/GNSS integration. The second one relates to our experience in their application in onboard airborne navigation algorithms developed by Moscow Institute of Electromechanics and Automatics.

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