Safe Position Estimation for Train-borne Localisation using INS and Digital Map Data

2021 ◽  
Author(s):  
Andreas Wenz ◽  
Weiss Ohrendorf-Weiss
Keyword(s):  
Position Estimation ◽  
Digital Map ◽  
Map Data

Navigational Data Change Detection Based on Rasterizing Features & Attributes

2012 ◽  
Vol 256-259 ◽  
pp. 2279-2284
Author(s):  
Lian Ying Li ◽  
Zhang Huang ◽  
Xiao Lan Xu
Keyword(s):  
Change Detection ◽  
Detection Methods ◽  
Vehicle Navigation ◽  
Digital Map ◽  
Navigation Data ◽  
Data Production ◽  
History Of ◽  
Map Data ◽  
Data Updating

A necessary updating degree is vital for the digital map data in a vehicle navigation system. Only when the digital map data are well updated, can the quality of the navigation be assured. Today the companies devoting to the production of digital map data for vehicle navigation have to cost much labor, material and capital to collect and update data in order to maintain a necessary updating degree. Throughout the history of electronic navigation data updating, they have made considerable progress both on the methods and processes of data production, and the way of map management. Updating from the CD to the network, from the wired to the wireless, from the replacing to the incremental way, each of the technical changes is a power source to enhance the data updating rate. As we all know, the change detection is a prerequisite and base for the electronic navigation data updating. By rapidly developing the area with changes and using the appropriate updating method, we can scientifically maintain the original database of navigation data and terminal physical data. In view of this, starting from application needs for dynamic data updating, this paper analyses change detection methods of navigation data in different versions used for generating incremental data, and focuses on that of rasterizing features and attributes, exploring a new approach to quickly get the incremental data between versions.

scholarly journals Validation of Map Matching Algorithms using High Precision Positioning with GPS

2005 ◽  
Vol 58 (2) ◽  
pp. 257-271 ◽  
Author(s):  
Mohammed A. Quddus ◽  
Robert B. Noland ◽  
Washington Y. Ochieng
Keyword(s):  
High Precision ◽  
Dead Reckoning ◽  
Vehicle Speed ◽  
Map Matching ◽  
Precision Positioning ◽  
Digital Map ◽  
Mm Algorithm ◽  
Essential Components ◽  
Vehicle Trajectory ◽  
Map Data

Map Matching (MM) algorithms are usually employed for a range of transport telematics applications to correctly identify the physical location of a vehicle travelling on a road network. Two essential components for MM algorithms are (1) navigation sensors such as the Global Positioning System (GPS) and dead reckoning (DR), among others, to estimate the position of the vehicle, and (2) a digital base map for spatial referencing of the vehicle location. Previous research by the authors (Quddus et al., 2003; Ochieng et al., 2003) has developed improved MM algorithms that take account of the vehicle speed and the error sources associated with the navigation sensors and the digital map data previously ignored in conventional MM approaches. However, no validation study assessing the performance of MM algorithms has been presented in the literature. This paper describes a generic validation strategy and results for the MM algorithm previously developed in Ochieng et al. (2003). The validation technique is based on a higher accuracy reference (truth) of the vehicle trajectory as determined by high precision positioning achieved by the carrier-phase observable from GPS. The results show that the vehicle positions determined from the MM results are within 6 m of the true positions. The results also demonstrate the importance of the quality of the digital map data to the map matching process.

Three Tiered Self-Localization of Two Position Estimation Using Three Dimensional Environment Map and Gyro-Odometry

2014 ◽  
Vol 26 (2) ◽  
pp. 196-203 ◽  
Author(s):  
Kazuya Okawa ◽  
Keyword(s):  
Three Dimensional ◽  
Position Estimation ◽  
Map Matching ◽  
Localization Method ◽  
Challenge Course ◽  
Environment Map ◽  
Improve Accuracy ◽  
Environment Maps ◽  
Using Data ◽  
Map Data

As in the Tsukuba Challenge, any robot that autonomously moves around outdoors must be capable of accurate self-localization. Among many existing methods for robot self-localization, the most widely used is for the robot to estimate its position by comparing it with prior map data actually acquired using its sensor while it moves around. Although we use such a self-localization method in this study, this paper proposes a new method to improve accuracy in robot self-localization. In environments with few detected objects, a lack of acquired data very likely will lead to a failure in map matching and to erroneous robot self-localization. Therefore, a method for robot self-localization that uses three-dimensional environment maps and gyro-odometry depending on the situation is proposed. Moreover, the effectiveness of the proposed method is confirmed by using data from the 2013 Tsukuba Challenge course.

The Use of Digital Map Data to Provide Enhanced Navigation and Displays for Poor Weather Penetration and Recovery

1993 ◽  
Vol 46 (2) ◽  
pp. 208-222 ◽  
Author(s):  
Peter J. Bennett
Keyword(s):  
World View ◽  
Three Dimensional ◽  
Weather Conditions ◽  
Digital Data ◽  
Three Dimensional Model ◽  
Digital Map ◽  
Position Information ◽  
Digital Terrain ◽  
Cultural Details ◽  
Map Data

A GEC Marconi Avionics/MoD joint–funded integrated, digital terrain system called ' PENETRATE ' (passive enhanced navigation with terrain referenced avionics) is in its fourth year of flight trials on a Hunter fast–jet aircraft at the Defence Research Agency, Farnborough. Although originally designed to enable military aircraft to penetrate enemy defences at low level in poor weather conditions and at night, the system also has a direct read across to civil air transport operations. The heart of the PENETRATE system is a digital data–store housing a three–dimensional model of the terrain including cultural details, obstructions and tactical intelligence information.PENETRATE incorporates terrain-referenced navigation to provide accurate position information relative to the ground contours. A sophisticated digital map displays navigation information and includes intelligence and intervisibility overlays. Head-up visual enhancement options are provided which can be tailored to the outside visibility. These comprise monochrome skeletal perspective displays which are superimposed on the outside world view and also on the forward-looking infra-red (FUR) scene. The display enhancements range from obstruction cues through ridge line overlays to skeletal perspective terrain presentations.This paper covers the philosophy and architecture of PENETRATE together with details of the demonstration system. It also discusses the capability of this digital terrain system to enhance the safety of civil aircraft approaches into difficult airports such as Kathmandu and Hong Kong.

scholarly journals Lane-Level Map-Aiding Approach Based on Non-Lane-Level Digital Map Data in Road Transport Security

2021 ◽  
Vol 13 (17) ◽  
pp. 9724
Author(s):  
Philipp Luz ◽  
Li Zhang ◽  
Jinyue Wang ◽  
Volker Schwieger
Keyword(s):  
Risk Estimation ◽  
Road Transport ◽  
Large Area ◽  
Crucial Point ◽  
Digital Map ◽  
Digital Maps ◽  
Navigation Data ◽  
Terror Attacks ◽  
Vehicle Position ◽  
Map Data

To prevent terror attacks in which trucks are used as weapons as happened in Nice or Berlin in 2016, the European Project Autonomous Emergency Maneuvering and Movement Monitoring for Road Transport Security (TransSec) was launched in 2018. One crucial point of this project is the development of a map-aiding approach for the localization of vehicles on digital maps, so that the information in digital map data can be used to detect prohibited driving maneuvers, such as off-road or wrong-way drivers. For example, a lane-level map-aiding approach is required for wrong-way driver detection. Navigation Data Standard (NDS) is one of the worldwide map standards developed by several automobile manufacturers. So far, there is no lane-level NDS map covers a large area, therefore, it was decided to use the latest available NDS map without lane level accuracy. In this paper, a lane-level map-aiding approach based on a non-lane-level NDS map is presented. Due to the inaccuracy of vehicle position and digital map the map-aiding does not always provide the correct results, so probabilities of off-road and wrong-way diver detection are estimated to support risk estimation. The performance of the developed map-aiding approach is comprehensively evaluated with both real and simulated trajectories.

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