scholarly journals Brain-Like Navigation Scheme based on MEMS-INS and Place Recognition

2019 ◽  
Vol 9 (8) ◽  
pp. 1708 ◽  
Author(s):  
Chong Shen ◽  
Xiaochen Liu ◽  
Huiliang Cao ◽  
Yuchen Zhou ◽  
Jun Liu ◽  
...  
Keyword(s):  
Error Correction ◽  
Flight Test ◽  
Micro Electro Mechanical System ◽  
Recognition Algorithm ◽  
Scene Recognition ◽  
Navigation Systems ◽  
Place Recognition ◽  
Position Errors ◽  
Navigation Scheme ◽  
Grid Based

Animals have certain cognitive competence about the environment so they can correct their navigation errors. Inspired by the excellent navigational behavior of animals, this paper proposes a brain-like navigation scheme to improve the accuracy and intelligence of Micro-Electro-Mechanical System based Inertial Navigation Systems (MEMS-INS). The proposed scheme employs vision to acquire external perception information as an absolute reference to correct the position errors of INS, which is established by analyzing the navigation and error correction mechanism of rat brains. In addition, to improve the place matching speed and precision of the system for visual scene recognition, this paper presents a novel place recognition algorithm that combines image scanline intensity (SI) and grid-based motion statistics (GMS) together which is named the SI-GMS algorithm. The proposed SI-GMS algorithm can effectively reduce the influence of uncertain environment factors on the recognition results, such as pedestrians and vehicles. It solves the problem that the matching result will occasionally go wrong when simply using the scanline intensity (SI) algorithm, or the slow matching speed when simply using grid-based motion statistics (GMS) algorithm. Finally, an outdoor Unmanned Aerial Vehicle (UAV) flight test is carried out. Based on the reference information from the high-precision GPS device, the results illustrate the effectiveness of the scheme in error correction of INS and the algorithm in place recognition.

scholarly journals Consistency of the Empirical Distributions of Navigation Positioning System Errors with Theoretical Distributions—Comparative Analysis of the DGPS and EGNOS Systems in the Years 2006 and 2014

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 31
Author(s):  
Mariusz Specht
Keyword(s):  
Normal Distribution ◽  
Root Mean Square ◽  
Statistical Tests ◽  
Position Error ◽  
Positioning System ◽  
Navigation Systems ◽  
Mean Square ◽  
Positioning Systems ◽  
Position Errors ◽  
System Errors

Positioning systems are used to determine position coordinates in navigation (air, land and marine). The accuracy of an object’s position is described by the position error and a statistical analysis can determine its measures, which usually include: Root Mean Square (RMS), twice the Distance Root Mean Square (2DRMS), Circular Error Probable (CEP) and Spherical Probable Error (SEP). It is commonly assumed in navigation that position errors are random and that their distribution are consistent with the normal distribution. This assumption is based on the popularity of the Gauss distribution in science, the simplicity of calculating RMS values for 68% and 95% probabilities, as well as the intuitive perception of randomness in the statistics which this distribution reflects. It should be noted, however, that the necessary conditions for a random variable to be normally distributed include the independence of measurements and identical conditions of their realisation, which is not the case in the iterative method of determining successive positions, the filtration of coordinates or the dependence of the position error on meteorological conditions. In the preface to this publication, examples are provided which indicate that position errors in some navigation systems may not be consistent with the normal distribution. The subsequent section describes basic statistical tests for assessing the fit between the empirical and theoretical distributions (Anderson-Darling, chi-square and Kolmogorov-Smirnov). Next, statistical tests of the position error distributions of very long Differential Global Positioning System (DGPS) and European Geostationary Navigation Overlay Service (EGNOS) campaigns from different years (2006 and 2014) were performed with the number of measurements per session being 900’000 fixes. In addition, the paper discusses selected statistical distributions that fit the empirical measurement results better than the normal distribution. Research has shown that normal distribution is not the optimal statistical distribution to describe position errors of navigation systems. The distributions that describe navigation positioning system errors more accurately include: beta, gamma, logistic and lognormal distributions.

An Improved and Efficient Algorithm for SINS/GPS/Doppler Integrated Navigation Systems

2012 ◽  
Vol 245 ◽  
pp. 323-329 ◽  
Author(s):  
Muhammad Ushaq ◽  
Jian Cheng Fang
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Sensor ◽  
Inertial Navigation ◽  
Measurement Unit ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Efficient Manner ◽  
Position Errors

Inertial navigation systems exhibit position errors that tend to grow with time in an unbounded mode. This degradation is due, in part, to errors in the initialization of the inertial measurement unit and inertial sensor imperfections such as accelerometer biases and gyroscope drifts. Mitigation to this growth and bounding the errors is to update the inertial navigation system periodically with external position (and/or velocity, attitude) fixes. The synergistic effect is obtained through external measurements updating the inertial navigation system using Kalman filter algorithm. It is a natural requirement that the inertial data and data from the external aids be combined in an optimal and efficient manner. In this paper an efficient method for integration of Strapdown Inertia Navigation System (SINS), Global Positioning System (GPS) and Doppler radar is presented using a centralized linear Kalman filter by treating vector measurements with uncorrelated errors as scalars. Two main advantages have been obtained with this improved scheme. First is the reduced computation time as the number of arithmetic computation required for processing a vector as successive scalar measurements is significantly less than the corresponding number of operations for vector measurement processing. Second advantage is the improved numerical accuracy as avoiding matrix inversion in the implementation of covariance equations improves the robustness of the covariance computations against round off errors.

scholarly journals CNN-BASED PLACE RECOGNITION TECHNIQUE FOR LIDAR SLAM

2020 ◽  
Vol XLIV-M-2-2020 ◽  
pp. 117-122
Author(s):  
Y. Yang ◽  
S. Song ◽  
C. Toth
Keyword(s):  
Urban Areas ◽  
Recognition Task ◽  
Dead Reckoning ◽  
Point Clouds ◽  
Navigation Systems ◽  
Place Recognition ◽  
3D Point Clouds ◽  
Localization And Mapping ◽  
Sensing Platform ◽  
3D Descriptors

Abstract. Place recognition or loop closure is a technique to recognize landmarks and/or scenes visited by a mobile sensing platform previously in an area. The technique is a key function for robustly practicing Simultaneous Localization and Mapping (SLAM) in any environment, including the global positioning system (GPS) denied environment by enabling to perform the global optimization to compensate the drift of dead-reckoning navigation systems. Place recognition in 3D point clouds is a challenging task which is traditionally handled with the aid of other sensors, such as camera and GPS. Unfortunately, visual place recognition techniques may be impacted by changes in illumination and texture, and GPS may perform poorly in urban areas. To mitigate this problem, state-of-art Convolutional Neural Networks (CNNs)-based 3D descriptors may be directly applied to 3D point clouds. In this work, we investigated the performance of different classification strategies utilizing a cutting-edge CNN-based 3D global descriptor (PointNetVLAD) for place recognition task on the Oxford RobotCar dataset.

The Estimation of Angular Misalignments for Ultra Short Baseline Navigation Systems. Part I: Numerical Simulations

2013 ◽  
Vol 66 (4) ◽  
pp. 561-578 ◽  
Author(s):  
Hsin-Hung Chen
Keyword(s):  
Numerical Simulations ◽  
Survey Method ◽  
Alignment Error ◽  
Navigation Systems ◽  
Misalignment Angle ◽  
Short Baseline ◽  
Position Errors ◽  
Positioning Errors ◽  
Line Survey ◽  
Survey Approach

Ultra Short Baseline (USBL) navigation systems are often faced with positioning errors arising from misalignments between sensors. This paper proposes a line survey method for USBL angular alignment calibration. In the scheme of USBL line survey, mathematical representations of positioning error arising from heading, pitch and roll misalignments are derived, respectively. The effect of each misalignment angle and how the differences can be used to calibrate each misalignment angle in turn are presented. An iterative algorithm that takes advantage of the geometry of position errors resulting from angular misalignments is developed for USBL calibration. Numerical simulations are provided to demonstrate the effectiveness of the USBL line survey approach. In addition, the effect of measurement error on the estimation of roll alignment error is evaluated and discussed.

On Navigation Systems for Motorcycles: The Influence and Estimation of Roll Angle

2005 ◽  
Vol 58 (3) ◽  
pp. 375-388 ◽  
Author(s):  
Joshua P. Coaplen ◽  
Patrick Kessler ◽  
Oliver M. O'Reilly ◽  
Dan M. Stevens ◽  
J. Karl Hedrick
Keyword(s):  
Global Positioning System ◽  
Angular Rate ◽  
Roll Angle ◽  
Estimation Methods ◽  
Navigation Systems ◽  
Simulation Environment ◽  
Global Positioning ◽  
Heading Angle ◽  
Navigation Information ◽  
Navigation Scheme

Vehicle navigation systems use various sensors and the global positioning system (GPS) to locate a vehicle. This location is then matched to a map database to provide navigation information. Between GPS updates, the vehicle's heading angle and forward speed are used to “dead reckon” its position. Heading angle is often measured by integrating the output of a rate gyroscope. For this measurement to be equal to the vehicle's heading angle, the vehicle should not experience any rotation about its roll or pitch axes. For an automobile, the roll and pitch angles are small and may be neglected for the purposes of navigation. This article demonstrates that this same assumption is not true for a motorcycle. Through simulation, it is shown that for a motorcycle, obtaining a meaningful heading angle from a single angular rate measurement requires accounting for the motorcycle's roll angle. Methods to estimate roll angle and heading angle from available navigation measurements are presented, and two possible sensor configurations are compared. A motorcycle navigation scheme based on these roll angle estimation methods is shown to produce exceptional results in a simulation environment.

Attitude algorithm and initial alignment method for SINS applied in short-range aircraft

2017 ◽  
Vol 31 (19-21) ◽  
pp. 1740081 ◽  
Author(s):  
Rong-Hui Zhang ◽  
Zhao-Cheng He ◽  
Feng You ◽  
Bo Chen
Keyword(s):  
Digital Signal Processor ◽  
Short Range ◽  
Digital Signal ◽  
Flight Test ◽  
Micro Electro Mechanical System ◽  
Flight Simulation ◽  
Engineering Practice ◽  
Alignment Method ◽  
Initial Alignment ◽  
Simulation Test

This paper presents an attitude solution algorithm based on the Micro-Electro-Mechanical System and quaternion method. We completed the numerical calculation and engineering practice by adopting fourth-order Runge–Kutta algorithm in the digital signal processor. The state space mathematical model of initial alignment in static base was established, and the initial alignment method based on Kalman filter was proposed. Based on the hardware in the loop simulation platform, the short-range flight simulation test and the actual flight test were carried out. The results show that the error of pitch, yaw and roll angle is fast convergent, and the fitting rate between flight simulation and flight test is more than 85%.

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