Hiba Al-Assaad, C. Boucher, A. Daher, Ahmad Shahin, J. Noyer
{"title":"Statistical modelling of digital elevation models for GNSS-based navigation","authors":"Hiba Al-Assaad, C. Boucher, A. Daher, Ahmad Shahin, J. Noyer","doi":"10.1080/19479832.2023.2218376","DOIUrl":null,"url":null,"abstract":"ABSTRACT Recently, smart mobility has become a important activity in transportation systems such as public, autonomous and shared transports. These systems require reliable navigation applications that lead to precise localisation and optimised route. The GPS system may face problems such as signal degradation caused by conical effects, affecting the reliability and accuracy of the signal, or signal loss in poor visibility environments. By using other sensors, the vehicle location system can overcome these GPS problems. This work focuses on the estimation of the inclination, which will be used to optimise the route planning for the EV or HEV especially in order to control the energy consumption. This paper presents a multi-sensor fusion method, based on GNSS, INS, OSM and DEM data fused using a non-linear particle filter, to estimate and improve the slopes of road segments. A new statistical modelling of the DEM errors related to the spatial sampling of elevation data is proposed. This method is based on the definition of a geometrical window, called Adjacent Sliding Window (ASW), which dynamically selects the elevation data in the vicinity of the road. The proposed method is evaluated in a suburban transport network. The experimental results show the benefits of the vehicle attitude and road slope estimation accuracies.","PeriodicalId":46012,"journal":{"name":"International Journal of Image and Data Fusion","volume":"14 1","pages":"205 - 224"},"PeriodicalIF":1.8000,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Image and Data Fusion","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/19479832.2023.2218376","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"REMOTE SENSING","Score":null,"Total":0}
引用次数: 1
Abstract
ABSTRACT Recently, smart mobility has become a important activity in transportation systems such as public, autonomous and shared transports. These systems require reliable navigation applications that lead to precise localisation and optimised route. The GPS system may face problems such as signal degradation caused by conical effects, affecting the reliability and accuracy of the signal, or signal loss in poor visibility environments. By using other sensors, the vehicle location system can overcome these GPS problems. This work focuses on the estimation of the inclination, which will be used to optimise the route planning for the EV or HEV especially in order to control the energy consumption. This paper presents a multi-sensor fusion method, based on GNSS, INS, OSM and DEM data fused using a non-linear particle filter, to estimate and improve the slopes of road segments. A new statistical modelling of the DEM errors related to the spatial sampling of elevation data is proposed. This method is based on the definition of a geometrical window, called Adjacent Sliding Window (ASW), which dynamically selects the elevation data in the vicinity of the road. The proposed method is evaluated in a suburban transport network. The experimental results show the benefits of the vehicle attitude and road slope estimation accuracies.
期刊介绍：
International Journal of Image and Data Fusion provides a single source of information for all aspects of image and data fusion methodologies, developments, techniques and applications. Image and data fusion techniques are important for combining the many sources of satellite, airborne and ground based imaging systems, and integrating these with other related data sets for enhanced information extraction and decision making. Image and data fusion aims at the integration of multi-sensor, multi-temporal, multi-resolution and multi-platform image data, together with geospatial data, GIS, in-situ, and other statistical data sets for improved information extraction, as well as to increase the reliability of the information. This leads to more accurate information that provides for robust operational performance, i.e. increased confidence, reduced ambiguity and improved classification enabling evidence based management. The journal welcomes original research papers, review papers, shorter letters, technical articles, book reviews and conference reports in all areas of image and data fusion including, but not limited to, the following aspects and topics: • Automatic registration/geometric aspects of fusing images with different spatial, spectral, temporal resolutions; phase information; or acquired in different modes • Pixel, feature and decision level fusion algorithms and methodologies • Data Assimilation: fusing data with models • Multi-source classification and information extraction • Integration of satellite, airborne and terrestrial sensor systems • Fusing temporal data sets for change detection studies (e.g. for Land Cover/Land Use Change studies) • Image and data mining from multi-platform, multi-source, multi-scale, multi-temporal data sets (e.g. geometric information, topological information, statistical information, etc.).
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