Prediction of Real-Time Kinematic Positioning Availability on Road Using 3D Map and Machine Learning
Prediction of Real-Time Kinematic Positioning Availability on Road Using 3D Map and Machine Learning
复制标题
使用 3D 地图和机器学习预测道路上实时运动定位可用性
DOI：
10.1007/s13177-023-00352-6
复制
发表时间：
2023
影响因子：
2.1
通讯作者：
Nobuaki Kubo
中科院分区：
文献类型：
--
作者：
Kaito Kobayashi;Nobuaki Kubo
实时运动学（RTK）定位是一种精确的定位方法，有望支持自动驾驶。然而，众所周知，RTK的可用性在很大程度上取决于全球导航卫星系统（GNSS）的信号环境，而GNSS信号环境受建筑物和隧道高架桥的影响。在行驶前，利用三维地图模拟GNSS信号环境，预测RTK的可用性，是非常方便的。了解RTK对其他传感器的限制也很重要。因此，我们基于过去的试驾和模拟信号环境数据集，使用机器学习进行预测。在东京进行的两次评估测试中，预测准确率接近65-80%，并且我们发现了RTK可用性预测需要考虑的几个新问题。
Real-Time Kinematic (RTK) positioning is a precise positioning method, which is expected to support self-driving. However, it is known that the availability of RTK highly depends on the Global Navigation Satellite System (GNSS) signal environment, which is influenced by buildings and viaduct of tunnel. Before driving, it is convenience if we can simulate the GNSS signal environment using a three-dimensional (3D) map and predict the availability of RTK. It is also important to know the limitation of RTK for other sensors. Therefore, we predicted it using machine learning based on the past test-driving and simulated signal environment datasets. The prediction accuracy was almost 65–80% from two evaluation tests in Tokyo and we found several new issues to consider for RTK availability prediction.
GNSS/INS 传感器与车载传感器融合
DOI：
10.33012/2020.17611
发表时间：
2020
期刊：
Proceedings of the 33rd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2020)
影响因子：
--
作者：
Ryan Dixon;M. Bobye;Brett Kruger;J. Jacox
通讯作者：
J. Jacox
高原
DOI：
10.1007/978-3-642-41714-6_162456
发表时间：
2019
期刊：
Dictionary of Geotourism
影响因子：
--
作者：
T. Lorraine
通讯作者：
T. Lorraine