Publications
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Thio, Vincent Martello Kwie Hoe; Aparicio, Joaquin; Ånonsen, Kjetil Bergh & Bekkeng, Jan Kenneth
(2021).
Experimental Evaluation of the Forkbeard Ultrasonic Indoor Positioning System.
IEEE Transactions on Instrumentation and Measurement.
ISSN 0018-9456.
doi:
10.1109/TIM.2021.3136261.
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Indoor positioning systems are crucial to provide location based services in areas that are not covered by a Global Navigation Satellite System. Among the many technologies applied to this field, ultrasound has emerged as a potential low-cost, high-accuracy approach to positioning based on trilateration. Several ultrasonic systems have been proposed over the years. Of these, academic systems are typically prototypes that are unavailable to the public, whereas commercial systems generally do not provide characterization test results. In this work, we have conducted a detailed characterization study of a commercial indoor positioning system for smart devices developed by Forkbeard Technologies AS. We tested the system under static and dynamic conditions in a motion capture lab of approximately 150 m2. We considered different room occupancies, beacon configurations, and device positions. The results, given in terms of 2D absolute errors at different confidence levels, show great variation depending on the aforementioned conditions. The worst case scenario corresponds to a pedestrian in motion in an office setup with 4 beacons, with the phone placed in the pocket. In this case, 80% of the errors were below 143 cm. The best results were obtained under static conditions using 10 beacons, for which 80% of the errors were below 44 cm.
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Thio, Vincent Martello Kwie Hoe; Aparicio, Joaquin; Ånonsen, Kjetil Bergh; Bekkeng, Jan Kenneth & Booij, Wilfred
(2021).
Fusing of a continuous output PDR algorithm with an ultrasonic positioning system.
IEEE Sensors Journal.
ISSN 1530-437X.
doi:
10.1109/JSEN.2021.3134468.
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The availability of inertial navigation sensors in smartphones has facilitated the development of pedestrian dead reckoning (PDR) models on a large scale. These models often consist of a step detection algorithm combined with a heading estimation routine. Common approaches to step detection include searching for peaks/valleys in the acceleration signal, principle frequency estimation, and machine learning techniques. Since the sensors embedded in smart devices are prone to noise, the position error grows unbounded if unchecked and requires periodical corrections based on external measurements. In this work, we propose a novel step detection algorithm based on sine-wave approximation of the acceleration signal. This method detects step fractions as well as full steps, which allows for continuous and real-time updates. The step detection algorithm is combined with a heading estimation routine described in our previous work to obtain a stand-alone PDR model. To mitigate error accumulation, we fuse the proposed model with position and heading measurements provided by a commercial indoor positioning system based on ultrasound. We evaluate the performance of the PDR and fused model in an open office environment, by walking along a trajectory while carrying a smartphone in hand or in the pocket. The results demonstrate the feasibility of the sine-wave approximation approach to step detection, as well as the expected benefits of fusing PDR with the ultrasonic system.
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Thio, Vincent; Ånonsen, Kjetil Bergh & Bekkeng, Jan Kenneth
(2021).
Relative Heading Estimation for Pedestrians Based on the Gravity Vector.
IEEE Sensors Journal.
ISSN 1530-437X.
doi:
10.1109/JSEN.2021.3052430.
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Inertial navigation of pedestrians carrying a smart device is a core component of many indoor positioning systems. While infrastructure-based solutions typically depend on an installation of dedicated hardware, inertial navigation depends only on sensors embedded in the device itself. A single solution can thus be applied to a large range of use cases. This work focuses on one of the main challenges in inertial navigation: user heading estimation. We describe a complete statistical model for heading estimation based on the IMU and magnetometer, assuming a fixed device pose on the pedestrian. Our aim is to provide a stand-alone solution, suitable for direct implementation into a larger positioning framework. The method consists of two consecutive parts. The first focuses on gravity vector estimation based on IMU data. We describe a method for obtaining independent estimates under dynamic conditions, thereby removing the quasi-static initialization phase required by conventional methods. The second part combines the gravity vector with gyro and magnetic measurements to estimate user heading. The proposed method is tested against a motion capture system, and against an alternative method based on attitude. We find that both methods produce similar results in terms of accuracy.
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Published
Feb. 14, 2018 2:42 PM
- Last modified
Oct. 22, 2019 11:02 AM