One New Map Matching Model for Vehicle-Borne Navigation System

2010 ◽  
Author(s):  
Youwen Liu
Keyword(s):  
Navigation System ◽  
Map Matching ◽  
Matching Model

scholarly journals Lane-level localization system using surround-view cameras adaptive to different driving conditions

2020 ◽  
Vol 17 (2) ◽  
pp. 172988142092163
Author(s):  
Tianyi Li ◽  
Yuhan Qian ◽  
Arnaud de La Fortelle ◽  
Ching-Yao Chan ◽  
Chunxiang Wang
Keyword(s):  
Deep Learning ◽  
Low Cost ◽  
Lateral Position ◽  
Measurement Unit ◽  
Map Matching ◽  
Matching Model ◽  
Localization System ◽  
Perceptual Model ◽  
Road Map ◽  
Driving Conditions

This article presents a lane-level localization system adaptive to different driving conditions, such as occlusions, complicated road structures, and lane-changing maneuvers. The system uses surround-view cameras, other low-cost sensors, and a lane-level road map which suits for mass deployment. A map-matching localizer is proposed to estimate the probabilistic lateral position. It consists of a sub-map extraction module, a perceptual model, and a matching model. A probabilistic lateral road feature is devised as a sub-map without limitations of road structures. The perceptual model is a deep learning network that processes raw images from surround-view cameras to extract a local probabilistic lateral road feature. Unlike conventional deep-learning-based methods, the perceptual model is trained by auto-generated labels from the lane-level map to reduce manual effort. The matching model computes the correlation between the sub-map and the local probabilistic lateral road feature to output the probabilistic lateral estimation. A particle-filter-based framework is developed to fuse the output of map-matching localizer with the measurements from wheel speed sensors and an inertial measurement unit. Experimental results demonstrate that the proposed system provides the localization results with submeter accuracy in different driving conditions.

Implementation of generic algorithm in map-matching model

2017 ◽  
Vol 72 ◽  
pp. 283-292 ◽  
Author(s):  
Marko Nikolić ◽  
Jadranka Jović
Keyword(s):  
Map Matching ◽  
Matching Model ◽  
Generic Algorithm

POMM: Precise Overpass Map-Matching Model and Algorithm

2012 ◽  
Vol 457-458 ◽  
pp. 1213-1218 ◽  
Author(s):  
Zhen Xing Zhu ◽  
Jian Ping Xing ◽  
De Qiang Wang
Keyword(s):  
Topological Structure ◽  
Road Networks ◽  
Gps Data ◽  
Map Matching ◽  
Matching Model ◽  
Matching Algorithm ◽  
Actual Position ◽  
The Common

Current map-matching algorithms consider more about the common plain road networks. The overpass always be ignored or treated as normal intersection without considering its complex topological structure. In order to fill this gap in map-matching area, the POMM (Precise Overpass Map-matching Model and Algorithm) is proposed in this paper. A novel overpass model is built for the overpasses map-matching algorithm. This model divided the overpass into straight roads and curve ones which consist of a set of directional points. According to the match degree for each straight road or directional point, the optimum road can be selectd from the candidate roads. Finally, the vehicle can be matched to the actual position on the optimum road. Experiment results of Jinan Bayi overpass using the actual GPS data shows that the algorithm has efficiency in accuracy (over 95%) and can precisely find the actual position of the vehicle in the overpass road, especially for the curve roads.

scholarly journals Development of Edge-Node Map Based Navigation System Without Requirement of Prior Sensor Data Collection

2020 ◽  
Vol 32 (6) ◽  
pp. 1112-1120
Author(s):  
Kazuki Takahashi ◽  
◽  
Jumpei Arima ◽  
Toshihiro Hayata ◽  
Yoshitaka Nagai ◽  
...  
Keyword(s):  
Navigation System ◽  
Point Cloud ◽  
Dynamic Environment ◽  
Sensor Data ◽  
Map Matching ◽  
Edge Node ◽  
The Road ◽  
Autonomous Robot Navigation ◽  
On The Road ◽  
Target Environment

In this study, a novel framework for autonomous robot navigation system is proposed. The navigation system uses an edge-node map, which is easily created from electronic maps. Unlike a general self-localization method using an occupancy grid map or a 3D point cloud map, there is no need to run the robot in the target environment in advance to collect sensor data. In this system, the internal sensor is mainly used for self-localization. Assuming that the robot is running on the road, the position of the robot is estimated by associating the robot’s travel trajectory with the edge. In addition, node arrival determination is performed using branch point information obtained from the edge-node map. Because this system does not use map matching, robust self-localization is possible, even in a dynamic environment.

A Map Matching Algorithm Based on Curvature and Slope

2012 ◽  
Vol 594-597 ◽  
pp. 2390-2393
Author(s):  
Wei Ying Wu ◽  
Chuan Li Kang
Keyword(s):  
Navigation System ◽  
Vehicle Navigation ◽  
Route Guidance ◽  
Map Matching ◽  
Matching Algorithm ◽  
Vehicle Navigation System ◽  
Road Condition ◽  
Path Matching

In vehicle navigation system, path matching often go wrong when road condition is complex, especially there are island ring, or exit and entrance of highway. This lead to wrong map display and wrong route guidance, thus system give a bad impact to driver. In order to solve this problem, this paper includes a map matching algorithm based on curvature and slope. This Algorithm coding has been realized by c and proved its availability at last.

Navigation Based on Sensors in Smartphones

2018 ◽  
pp. 368-396
Author(s):  
Guenther Retscher ◽  
Allison Kealy
Keyword(s):  
Navigation System ◽  
Inertial Navigation ◽  
Dead Reckoning ◽  
Activity Monitoring ◽  
Navigation Systems ◽  
Location Tracking ◽  
Map Matching ◽  
Complex Environments ◽  
Positioning Systems ◽  
Cooperative Positioning

With the increasing ubiquity of smartphones and tablets, users are now routinely carrying a variety of sensors with them wherever they go. These devices are enabling technologies for ubiquitous computing, facilitating continuous updates of a user's context. They have built-in MEMS-based accelerometers for ubiquitous activity monitoring and there is a growing interest in how to use these together with gyroscopes and magnetometers to build dead reckoning (DR) systems for location tracking. Navigation in complex environments is needed mainly by consumer users, private vehicles, and pedestrians. Therefore, the navigation system has to be small, easy to use, and have reasonably low levels of power consumption and price. The technologies and techniques discussed here include the fusion of inertial navigation (IN) and other sensors, positioning based on signals from wireless networks (such as Wi-Fi), image-based methods, cooperative positioning systems, and map matching (MM). The state-of-the-art of MEMS-based location sensors and their integration into modern navigation systems are also presented.

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