Statistical modelling of digital elevation models for GNSS-based navigation

IF 1.8 Q3 REMOTE SENSING

International Journal of Image and Data Fusion Pub Date : 2023-06-05 DOI:10.1080/19479832.2023.2218376

Hiba Al-Assaad, C. Boucher, A. Daher, Ahmad Shahin, J. Noyer

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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.

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gnss导航数字高程模型的统计建模

摘要近年来，智能出行已成为公共交通、自主交通和共享交通等交通系统中的一项重要活动。这些系统需要可靠的导航应用程序，从而实现精确定位和优化路线。GPS系统可能面临诸如锥形效应引起的信号退化、影响信号的可靠性和准确性、或者在能见度低的环境中信号丢失等问题。通过使用其他传感器，车辆定位系统可以克服这些GPS问题。这项工作的重点是倾斜度的估计，这将用于优化电动汽车或HEV的路线规划，特别是为了控制能耗。本文提出了一种基于非线性粒子滤波器融合GNSS、INS、OSM和DEM数据的多传感器融合方法，以估计和改善路段的坡度。提出了一种与高程数据空间采样相关的DEM误差的新统计模型。该方法基于一个称为相邻滑动窗口（ASW）的几何窗口的定义，该窗口动态选择道路附近的高程数据。在郊区交通网络中对所提出的方法进行了评估。实验结果表明了车辆姿态和道路坡度估计精度的优越性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Image and Data Fusion REMOTE SENSING-

CiteScore

5.00

自引率

0.00%

发文量

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.).