Evaluation of MEMS sensors accuracy for bicycle tracking and positioning

Miah, Shahjahan; Kaparias, Ioannis; Liatsis, Panos

doi:10.1109/iwssip.2015.7314235

Cited by 5 publications

(3 citation statements)

References 6 publications

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“…In such a model, the measurement error on the accelerometer will indeed be propagated proportionally to the square of the integration time considered and that of the gyrometer to the cube of this time. A study of bicycle trajectory estimation performances by integration of MEMS data is carried out in [3]. It is shown that this solution alone is insufficient (the value of the angular estimate drifts by 13.3 %) and needs to be completed by the use of filtering, modeling or assumptions.…”

Section: Fig 1: Principle Of Inertial Navigationmentioning

confidence: 99%

“…The main objective is not only navigation and itinerary proposal, but also cyclist safety. Indeed, the bicycle is a transportation mode that is perceived as dangerous by users, that is why many works are interested in cyclist localization and in the exchange of information between road users [1]- [3]. The accuracy required for the location of the cyclist is about one meter in order to be able to distinguish a user riding on the road, on a sidewalk or on a bike path.…”

Section: Introductionmentioning

confidence: 99%

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BIKES: Bicycle Itinerancy Kalman Filter With Embedded Sensors for Challenging Urban Environment

Perul¹,

Renaudin

2022

IEEE Sensors J.

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The use of bicycles is regaining popularity, especially in city centers where they can be used as quickly as cars and reduce carbon footprint. However, in these dense urban environments, navigation methods based on GNSS technologies do not provide sufficient accuracy for cyclist navigation and safety. To mitigate the challenges of indoor-like surroundings, a new positioning algorithm: BIKES (Bicycle Itinerancy Kalman filter with Embedded Sensors) was developed. This extended Kalman filter processes deeply degraded GNSS data to update velocity and position estimates with differential computation approaches working even in degraded environments. GNSS signals are combined with inertial and magnetic data to continuously estimate the trajectory when GNSS is unavailable. BIKES' performance was tested in real-life conditions on a 3 km long path in the city center downtown Nantes and compared to Google Fused Location Provider estimates. A mean positioning error below 1 m with a 0.5 m standard deviation is achieved. These results are 4 times better than the Google solution. This algorithm allows also distinguishing if the cyclist is riding on a bike path, the sidewalk, or the road, which is critical for guidance systems.

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Section: Fig 1: Principle Of Inertial Navigationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

BIKES: Bicycle Itinerancy Kalman Filter With Embedded Sensors for Challenging Urban Environment

Perul¹,

Renaudin

2022

IEEE Sensors J.

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“…More information about accuracy and measurement rates of each sensor is beyond the scope of this paper and can be found in [30]. However, measurement noise, assumed to be of Gaussian distribution, was taken into account at present by onetime experimental calibration for the approximation of the covariance matrices in the implementation of the Kalman filters.…”

Section: B Bicycle Position Kalman Filtermentioning

confidence: 99%

An Innovative Multi-Sensor Fusion Algorithm to Enhance Positioning Accuracy of an Instrumented Bicycle

Miah

Milonidis

Kaparias

et al. 2020

IEEE Trans. Intell. Transport. Syst.

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Cycling is an increasingly popular mode of travel in cities, but its poor safety record currently acts as a hurdle to its wider adoption as a real alternative to the private car. A particular source of hazard appears to originate from the interaction of cyclists with motorised traffic at low speeds in urban areas. But while technological advances in recent years have resulted in numerous attempts at systems for preventing cyclist-vehicle collisions, these have generally encountered the challenge of accurate cyclist localisation. This paper addresses this challenge by introducing an innovative bicycle localisation algorithm, which is derived from the geometrical relationships and kinematics of bicycles. The algorithm relies on the measurement of a set of kinematic variables (such as yaw, roll and steering angles) through low-cost on-board sensors. It then employs a set of Kalman filters to predict-correct the direction and position of the bicycle and fuse the measurements in order to improve positioning accuracy. The capabilities of the algorithm are then demonstrated through a real-world field experiment using an instrumented bicycle, called "iBike", in an urban environment. The results show that the proposed fusion achieves considerably lower positioning errors than would be achieved based on dead-reckoning alone, which makes the algorithm a credible basis for the development of future collision warning and avoidance systems.

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