摘要
机器人和机器学习的新闻编辑每日新闻-机器人的新研究-机器人和D自动化是一篇报道的主题。根据NewsRx记者来自西班牙阿利坎特的新闻报道，研究表明，“基于特征的地理定位依赖于将从空中图像中提取的特征与车辆传感器检测到的特征联系起来。这就要求必须从两个来源都能观察到陆地标志的类型。”这项研究的财政支持来自瓦伦西亚地区社区政府。我们的新闻编辑从Alica Nte大学的研究中获得了一句话：“这种缺乏多样性的特征类型产生了糟糕的表现，分别导致了由模糊性和缺乏检测产生的异常值和偏差。为了缓解这些缺点，在这封信中，本文提出了一种动态加权因子图模型,该模型的权值调整依赖于激光雷达传感器探测到的信息量化,模型中包含了(GNSS-based)的先验误差估计,当表示变得稀疏或稀疏时,根据修正后的PRI或轨迹动态调整权值。通过这种方式减少异常值和偏差。我们将我们的ME方法与最先进的地理定位方法进行比较，在一个具有挑战性和模糊性的美国环境中，我们也会导致检测损失。根据新闻编辑的说法，研究得出的结论是：“在其他方法失败的情况下，我们证明了上述缺陷的缓解。”
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Robotics - Robotics an d Automation is the subject of a report. According to news reporting originating from Alicante, Spain, by NewsRx correspondents, research stated, “Feature-based geo-localization relies on associating features extracted from aerial imagery w ith those detected by the vehicle’s sensors. This requires that the type of land marks must be observable from both sources.” Financial support for this research came from Regional Valencian Community Gover nment. Our news editors obtained a quote from the research from the University of Alica nte, “This lack of variety of feature types generates poor representations that lead to outliers and deviations produced by ambiguities and lack of detections, respectively. To mitigate these drawbacks, in this letter, we present a dynamica lly weighted factor graph model for the vehicle’s trajectory estimation. The wei ght adjustment in this implementation depends on information quantification in t he detections performed using a LiDAR sensor. Also, a prior (GNSS-based) error e stimation is included in the model. Then, when the representation becomes ambigu ous or sparse, the weights are dynamically adjusted to rely on the corrected pri or trajectory, mitigating outliers and deviations in this way. We compare our me thod against state-of-the-art geo-localization ones in a challenging and ambiguo us environment, where we also cause detection losses.” According to the news editors, the research concluded: “We demonstrate mitigatio n of the mentioned drawbacks where the other methods fail.”
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