Abstract
Indoor positioning has recently become an important field of interest because global navigation satellite systems (GNSS) are usually unavailable in indoor environments. Pedestrian dead reckoning (PDR) is a promising localization technique for indoor environments since it can be implemented on widely used smartphones equipped with low cost inertial sensors. However, the PDR localization severely suffers from the accumulation of positioning errors, and other external calibration sources should be used. In this paper, a context-recognition-aided PDR localization model is proposed to calibrate PDR. The context is detected by employing particular human actions or characteristic objects and it is matched to the context pre-stored offline in the database to get the pedestrian’s location. The Hidden Markov Model (HMM) and Recursive Viterbi Algorithm are used to do the matching, which reduces the time complexity and saves the storage. In addition, the authors design the turn detection algorithm and take the context of corner as an example to illustrate and verify the proposed model. The experimental results show that the proposed localization method can fix the pedestrian’s starting point quickly and improves the positioning accuracy of PDR by 40.56% at most with perfect stability and robustness at the same time.
Highlights
Nowadays, with the rapid development of computing technology, the demand for location based services (LBS) is rapidly increasing [1]
In this paper, we mainly concentrate on Personal Dead-reckoning (PDR), in which only the inertial sensor of the phone is used [6]
Compared to traditional map matching and fingerprint algorithms, this method needs less information which can be measured directly and adjusted quickly whenever the map changes, and it is more reliable because the geographical features are more stable than Wi-Fi or Bluetooth signals
Summary
With the rapid development of computing technology, the demand for location based services (LBS) is rapidly increasing [1]. Various novel indoor localization techniques have been proposed, such as infrared light, Bluetooth, ultrasound, wireless local area networks (WLAN) [3], micro-electro-mechanical system (MEMS) and cellular network [4]. Among these methods, techniques based on smartphone sensors have attracted much more attention because of the popularity of mobile phones [5]. In this paper, we mainly concentrate on Personal Dead-reckoning (PDR), in which only the inertial sensor of the phone is used [6] This relative localization method measures and tracks the momentary location and trajectory of a walking person dependently using the smartphone without any external sensors
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