Current Issue : October - December Volume : 2017 Issue Number : 4 Articles : 5 Articles
The use of Global Navigation Satellite System (GNSS) data for land vehicle gravimetry tests\nis challenged by complicated environments. A new approach for land vehicle gravimetry using a\nStrapdown Inertial Navigation System and velometer-integrated navigation computation (SINS/VEL)\nwithout using GNSS information has been put forward. Aided by the velometer with continuous\nlongitudinal velocity output instead of GNSS signals, a SGA-WZ02 strapdown gravimeter that used\nthe SINS/VEL method was tested in 2015. Four repeated lines were measured along a south-north\ndirection highway in Eastern Changsha to verify the new method�s feasibility and performance. The\ngravity disturbance results showed an internal accuracy in scalar gravimetry about 1.17 mGal and\n1.91 mGal for external accuracy assessment, with a spatial resolution of 1.7 km. Comparing this new\nmethod with the traditional SINS/GNSS gravimetry approach, it appeared that the results using\nSINS/VEL showed comparable internal and external accuracy. Theoretical analysis and practical test\nresults showed that the new method was feasible for gravity determination by land dynamic vehicle....
Avision/inertia integrated positioningmethod using position and orientationmatchingwhich can be adopted on intelligent vehicle\nsuch as automated guided vehicle (AGV) and mobile robot is proposed in this work. The method is introduced firstly. Landmarks\nare placed into the navigation field and camera and inertial measurement unit (IMU) are installed on the vehicle. Vision processor\ncalculates the azimuth and position information from the pictures which include artificial landmarks with the known direction\nand position. Inertial navigation system (INS) calculates the azimuth and position of vehicle in real time and the calculated pixel\nposition of landmark can be computed from the INS output position. Then the needed mathematical models are established\nand integrated navigation is implemented by Kalman filter with the observation of azimuth and the calculated pixel position of\nlandmark. Navigation errors and IMU errors are estimated and compensated in real time so that high precision navigation results\ncan be got. Finally, simulation and test are performed, respectively. Both simulation and test results prove that this vision/inertia\nintegrated positioningmethod using position and orientationmatching has feasibility and it can achieve centimeter-level autonomic\ncontinuous navigation....
The rise of autonomous systems operating close to humans imposes new challenges in\nterms of robustness and precision on the estimation and control algorithms. Approaches based\non nonlinear optimization, such as moving horizon estimation, have been shown to improve the\naccuracy of the estimated solution compared to traditional filter techniques. This paper introduces\nan optimization-based framework for multi-sensor fusion following a moving horizon scheme.\nThe framework is applied to the often occurring estimation problem of motion tracking by fusing\nmeasurements of a global navigation satellite system receiver and an inertial measurement unit.\nThe resulting algorithm is used to estimate position, velocity, and orientation of a maneuvering\nairplane and is evaluated against an accurate reference trajectory. A detailed study of the influence\nof the horizon length on the quality of the solution is presented and evaluated against filter-like\nand batch solutions of the problem. The versatile configuration possibilities of the framework are\nfinally used to analyze the estimated solutions at different evaluation times exposing a nearly linear\nbehavior of the sensor fusion problem....
This paper presents an original technique for robust detection of line features from range data, which is also the core element of\nan algorithm conceived for mapping 2D environments. A new approach is also discussed to improve the accuracy of position and\nattitude estimates of the localization by feeding back angular information extracted from the detected edges in the updating map.\nThe innovative aspects of the line detection algorithm regard the proposed hierarchical clusterization method for segmentation.\nInstead, line fitting is carried out by exploiting the Principal Component Analysis, unlike traditional techniques relying on least\nsquares linear regression. Numerical simulations are purposely conceived to compare these approaches for line fitting. Results\ndemonstrate the applicability of the proposed technique as it provides comparable performance in terms of computational load\nand accuracy compared to the least squares method. Also, performance of the overall line detection architecture, as well as of\nthe solutions proposed for line-based mapping and localization-aiding, is evaluated exploiting real range data acquired in indoor\nenvironments using an UTM-30LX-EW 2D LIDAR. This paper lies in the framework of autonomous navigation of unmanned\nvehicles moving in complex 2D areas, for example, being unexplored, full of obstacles, GPS-challenging, or denied....
Passive magnetic sensors measure the magnetic field density in three axes and are often integrated on a single chip. These low-cost\nsensors are widely used in car navigation as well as in battery powered navigation equipment such as smartphones as part of an\nelectronic compass. We focus on a train localization application with multiple, exclusively onboard sensors and a track map.This\napproach is considered as a base technology for future railway applications such as collision avoidance systems or autonomous train\ndriving. In this paper, we address the following question: how beneficial are passive magnetic measurements for train localization?\nWe present and analyze measurements of two differentmagnetometers recorded on a regional train at regular passenger service.We\nshow promising correlations of the measurements with the track positions and the traveled switch way.The processed data reveals\nthat the railway environment has repeatable, location-dependent magnetic signatures. This is considered as a novel approach to\ntrain localization, as the use of these magnetic signals at first view is not obvious. The proposed methods based on passive magnetic\nmeasurements show a high potential to be integrated in new and existing train localization approaches....
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