Abstract
Abstract. The increasing demand for reliable indoor navigation systems is leading the research community to investigate various approaches to obtain effective solutions usable with mobile devices. Among the recently proposed strategies, Ultra-Wide Band (UWB) positioning systems are worth to be mentioned because of their good performance in a wide range of operating conditions. However, such performance can be significantly degraded by large UWB range errors; mostly, due to non-line-of-sight (NLOS) measurements. This paper considers the integration of UWB with vision to support navigation and mapping applications. In particular, this work compares positioning results obtained with a simultaneous localization and mapping (SLAM) algorithm, exploiting a standard and a Time-of-Flight (ToF) camera, with those obtained with UWB, and then with the integration of UWB and vision. For the latter, a deep learning-based recognition approach was developed to detect UWB devices in camera frames. Such information is both introduced in the navigation algorithm and used to detect NLOS UWB measurements. The integration of this information allowed a 20% positioning error reduction in this case study.
Highlights
The development of GNSS (Global Navigation Satellite Systems) allowed any person with a proper receiver to assess his/her own position in real time at almost any outdoor location worldwide
The global location based services (LBS) market quickly increased its size in the last decade, and it is still expected to grow at 36% compound annual growth rate till 2023 (Market Reports World, 2019)
In the results reported in the following, this approach will be shortly named “uwb+rec”, where “rec” stands for anchor recognition
Summary
The development of GNSS (Global Navigation Satellite Systems) allowed any person with a proper receiver to assess his/her own position in real time at almost any outdoor location worldwide. Reliable and accurate positioning in GNSS-denied environments is a rather challenging task, which is attracting a lot of attention from the research community. Such interest is motivated by the LBS market on average, people spend approximately 90 percent of their time indoors (Scribner, 2018), and a significant increase of the LBS market is expected to come from an extension of LBS to indoors, which is obviously possible only if an effective ubiquitous navigation strategy is developed; e.g., able to properly deal with indoor navigation and with the transition between outdoors and indoors positioning
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