
This paper explores the possibility of using WiFi localization techniques for autonomous free-flying robots on the International Space Station (ISS). We have collected signal strength samples from the ISS, built the WiFi map using Gaussian processes, implemented a localizer based on particle filters, and evaluated the performance. Our results show the average error of 1.59 meters, which is accurate enough to identify which ISS module the robot is currently in. However, we found that most errors occurred in some specific modules under the current WiFi settings. This paper describes the challenges of applying WiFi localization techniques to the ISS and suggests several approaches to achieve better performance.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 4 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
