Geodesy and Geoinformatics, HafenCity Universität, 20457 Hamburg, Germany.
Sensors (Basel). 2022 Apr 25;22(9):3289. doi: 10.3390/s22093289.
Inertial odometry is a typical localization method that is widely and easily accessible in many devices. Pedestrian positioning can benefit from this approach based on inertial measurement unit (IMU) values embedded in smartphones. Fitting the inertial odometry outputs, namely step length and step heading of a human for instance, with spatial information is an ubiquitous way to correct for the cumulative noises. This so-called map-matching process can be achieved in several ways. In this paper, a novel real-time map-matching approach was developed, using a backtracking particle filter that benefits from the implemented geospatial analysis, which reduces the complexity of spatial queries and provides flexibility in the use of different kinds of spatial constraints. The goal was to generalize the algorithm to permit the use of any kind of odometry data calculated by different sensors and approaches as the input. Further research, development, and comparisons have been done by the easy implementation of different spatial constraints and use cases due to the modular structure. Additionally, a simple map-based optimization using transition areas between floors has been developed. The developed algorithm could achieve accuracies of up to 3 m at approximately the 90th percentile for two different experiments in a complex building structure.
惯性里程计是一种典型的定位方法，在许多设备中广泛且易于使用。基于智能手机中嵌入的惯性测量单元 (IMU) 值，行人定位可以受益于这种方法。将惯性里程计的输出（例如，人的步长和步向）与空间信息拟合，是纠正累积噪声的常用方法。这种所谓的地图匹配过程可以通过几种方式实现。在本文中，开发了一种新的实时地图匹配方法，使用回溯粒子滤波器，该滤波器受益于所实现的地理空间分析，从而降低了空间查询的复杂性，并在使用不同类型的空间约束方面提供了灵活性。目标是将算法推广，允许使用不同传感器和方法计算的任何类型的里程计数据作为输入。由于模块化结构，进一步的研究、开发和比较可以通过轻松实现不同的空间约束和用例来完成。此外，还开发了一种简单的基于地图的优化方法，使用楼层之间的过渡区域。在复杂的建筑物结构中进行的两项不同实验中，所开发的算法可以达到高达 3 米的精度，约为第 90 个百分位。
