The ability to accurately evaluate the performance of location determination systems is crucial for many applications. Typically, the performance of such systems is obtained by comparing ground truth locations with estimated locations. However, these ground truth locations are usually obtained by clicking on a map or using other worldwide available technologies like GPS. This introduces ground truth errors that are due to the marking process, map distortions, or inherent GPS inaccuracy. In this paper, we present a theoretical framework for analyzing the effect of ground truth errors on the evaluation of localization systems. Based on that, we design two algorithms for computing the real algorithmic error from the validation error and marking/map ground truth errors, respectively. We further establish bounds on different performance metrics. Validation of our theoretical assumptions and analysis using real data collected in a typical environment shows the ability of our theoretical framework to correct the estimated error of a localization algorithm in the presence of ground truth errors. Specifically, our marking error algorithm matches the real error CDF within 4%, and our map error algorithm provides a more accurate estimate of the median/tail error by 150%/72% when the map is shifted by 6m.
翻译:准确评估定位确定系统性能的能力对于许多应用来说至关重要。 一般来说,这类系统的性能是通过比较地面真实地点和估计地点而获得的。 但是,这些地面真相地点通常是通过点击地图或使用全球定位系统等其他世界性现有技术获得的。 这引入了由于标记过程、地图扭曲或固有的全球定位系统不准确而造成的地面真相错误。 在本文中,我们提出了一个理论框架,用于分析地面真相错误对本地化系统评估的影响。 在此基础上,我们设计了两种算法,分别用于从验证错误和标记/映射地面真实真相错误中计算真实的算法错误。我们进一步确定了不同性能指标的界限。使用在典型环境中收集的真实数据对我们的理论假设和分析进行验证,显示了我们理论框架在地面真相错误中纠正本地化算法估计错误的能力。 具体地说,我们的标记错误算法与实际错误CDF在4%之内匹配,而我们的地图错误算法则更准确地估计,在地图由6m移动时,中位/ 72%的中位/ 。