Abstract
Map-matching is a popular method that uses spatial information to improve the accuracy of positioning methods. The performance of map matching methods is closely related to spatial characteristics. Although several studies have demonstrated that certain map matching algorithms are affected by some spatial structures (e.g., parallel paths), they focus on the analysis of single map matching method or few spatial structures. In this study, we explored how the most commonly-used four spatial characteristics (namely forks, open spaces, corners, and narrow corridors) affect three popular map matching methods, namely particle filtering (PF), hidden Markov model (HMM), and geometric methods. We first provide a theoretical analysis on how spatial characteristics affect the performance of map matching methods, and then evaluate these effects through experiments. We found that corners and narrow corridors are helpful in improving the positioning accuracy, while forks and open spaces often lead to a larger positioning error. We hope that our findings are helpful for future researchers in choosing proper map matching algorithms with considering the spatial characteristics.
Highlights
While the Global Navigation Satellite Systems (GNSSs), such as GPS and Beidou, have been successfully applied in plenty of tasks, it does not work well in indoor environments since the satellite signals fail to pass through obstacles
This study focused on analyzing the effect of spatial characteristics of indoor spaces on three popular map matching methods: particle filtering (PF), hidden Markov model (HMM), and geometric methods
We evaluated the effect of different spatial characteristics on the three map matching methods, namely PF, HMM, and geometric method
Summary
While the Global Navigation Satellite Systems (GNSSs), such as GPS and Beidou, have been successfully applied in plenty of tasks, it does not work well in indoor environments since the satellite signals fail to pass through obstacles (e.g., buildings). To address the limitation of GNSS, a number of indoor positioning techniques have been proposed in recent years [1,2,3,4,5,6], including Wi-Fi, vision, light, and ultra-wideband (UWB), Bluetooth, and inertial sensors. To overcome the limitations of a single technique, hybrid methods that combine several techniques (e.g., Wi-Fi + Bluetooth) are often used [7,8,9,10,11,12], but the corresponding infrastructures may not be available in many environments. An effective way to improve the performance of indoor positioning methods is to fuse them with spatial information such as maps, which does not require additional hardware. Indoor spaces contain constraints in the form of geometric, topological, and semantic information, which can be used to constrain the trajectory of objects, thereby optimizing positioning results. A popular manner to utilize spatial information is map matching, which refines the preliminary positioning results with spatial information in maps or navigation models [13,14,15,16]
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