Study on Initial Gravity Map Matching Technique Based on Triangle Constraint Model

In this paper, a gravity map-matching algorithm is proposed based on a triangle constraint model. A high-accuracy triangle constraint model is constructed by using a short time and high-accuracy-featured inertial navigation system. In this paper, the principle of the gravity map-matching algorithm based on the triangle constraint model and a triangle matching parameter-parsing method are first introduced in detail. It is verified by test that the method is sensitive to the initial error value. By comparison to the commonly used Iterative Closest Contour Point (ICCP) and Sandia Inertial Terrain Aided Navigation (SITAN) algorithms respectively, the results show that this method is perfect in real-time performance and reliability, and its advantages are more obvious especially with a large initial error.

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A High Accuracy Fuzzy Logic Based Map Matching Algorithm for Road Transport

Journal of Intelligent Transportation Systems ◽

10.1080/15472450600793560 ◽

2006 ◽

Vol 10 (3) ◽

pp. 103-115 ◽

Cited By ~ 140

Author(s):

Mohammed A. Quddus ◽

Robert B. Noland ◽

Washington Y. Ochieng

Keyword(s):

Fuzzy Logic ◽

Road Transport ◽

High Accuracy ◽

Map Matching ◽

Matching Algorithm

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Improved ICCP Algorithm Considering Scale Error for Underwater Geomagnetic Aided Inertial Navigation

Mathematical Problems in Engineering ◽

10.1155/2019/1527940 ◽

2019 ◽

Vol 2019 ◽

pp. 1-9 ◽

Cited By ~ 1

Author(s):

Hongmei Zhang ◽

Le Yang ◽

Minglong Li

Keyword(s):

Navigation System ◽

Affine Transformation ◽

Inertial Navigation System ◽

Inertial Navigation ◽

Rigid Transformation ◽

Practical Application ◽

Matching Algorithm ◽

Scaling Parameters ◽

Scale Error ◽

Contour Point

The iterative closest contour point (ICCP) matching algorithm has become more and more widely used in the underwater geomagnetic aided inertial navigation system (INS). In practical application, the traditional ICCP algorithm is sensitive to the initial positioning error of the INS and can only do rigid transformation for the INS track of the vehicle. Particularly when there exists scale error, the accuracy and stability of the traditional ICCP algorithm will be affected. To solve this problem, an improved algorithm based on affine transformation is proposed. Firstly, the fundamental of the ICCP is analyzed in detail, and an error analysis of the geomagnetic aided inertial navigation system is carried out, and then the rigid transformation is replaced with affine transformation to improve the performance of the ICCP. In contrast to the conventional approach, the proposed algorithm can solve the rotation, translation, and scaling parameters of the indicated track and the matching track, so it can significantly reduce the interference of the scale error. Experimental results confirm the effectiveness of the proposed algorithm.

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The development of a High data rate atom interferometric gravimeter (HIDRAG) for gravity map matching navigation

2021 IEEE International Symposium on Inertial Sensors and Systems (INERTIAL) ◽

10.1109/inertial51137.2021.9430461 ◽

2021 ◽

Author(s):

Benjamin Adams ◽

Calum Macrae ◽

Mani Entezami ◽

Kevin Ridley ◽

Archie Kubba ◽

...

Keyword(s):

Data Rate ◽

High Data Rate ◽

Map Matching ◽

High Data ◽

Gravity Map

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Development of Multilayer-Based Map Matching to Enhance Performance in Large Truck Fleet Dispatching

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10020079 ◽

2021 ◽

Vol 10 (2) ◽

pp. 79

Author(s):

Ching-Yun Mu ◽

Tien-Yin Chou ◽

Thanh Van Hoang ◽

Pin Kung ◽

Yao-Min Fang ◽

...

Keyword(s):

Data Structures ◽

Spatial Data ◽

Spatial Information ◽

Data Retrieval ◽

Spatial Data Mining ◽

Map Matching ◽

Matching Algorithm ◽

Table Method ◽

Look Up Table ◽

The Look

Spatial information technology has been widely used for vehicles in general and for fleet management. Many studies have focused on improving vehicle positioning accuracy, although few studies have focused on efficiency improvements for managing large truck fleets in the context of the current complex network of roads. Therefore, this paper proposes a multilayer-based map matching algorithm with different spatial data structures to deal rapidly with large amounts of coordinate data. Using the dimension reduction technique, the geodesic coordinates can be transformed into plane coordinates. This study provides multiple layer grouping combinations to deal with complex road networks. We integrated these techniques and employed a puncture method to process the geometric computation with spatial data-mining approaches. We constructed a spatial division index and combined this with the puncture method, which improves the efficiency of the system and can enhance data retrieval efficiency for large truck fleet dispatching. This paper also used a multilayer-based map matching algorithm with raster data structures. Comparing the results revealed that the look-up table method offers the best outcome. The proposed multilayer-based map matching algorithm using the look-up table method is suited to obtaining competitive performance in identifying efficiency improvements for large truck fleet dispatching.

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