An advanced real-time navigation solution for cycling applications using portable devices
Notice bibliographique
Résumé
Obtaining an accurate navigation solution for cycling applications using portable devices without applying constraints is very challenging, especially during unavailability of absolute navigation information, such as from the Global Navigation Satellite Systems (GNSS). The main challenges are: (i) the device containing the sensors is not tethered to the moving platform, but rather moves with respect to the moving platform (here a bicycle), meaning the device could be on the body of the cyclist and undergo any type of motion dynamics as well as vibrations; (ii) the device, and consequently the frame of the sensors inside, can be in any orientation with respect to the direction of motion of the cycling platform, and this relative orientation (defined as misalignment between the device and platform or bicycle) can change at any time; and (iii) the error characteristics of the used low-cost inertial sensors lead to an increase in positon errors during the unavailability of absolute navigation information. This paper presents an accurate, continuous, and real-time navigation solution for portable devices in cycling applications. The proposed navigation solution utilizes new techniques to overcome the above mentioned challenges. This solution does not rely purely on the Inertial Navigation System (INS) when absolute navigation updates are unavailable, but uses Cycling Dead Reckoning (CDR) to update the INS solution. During GNSS availability, models for estimating speed from cycling frequency and travelled distance from the detected cycles are obtained. During GNSS outages, these models are used to estimate the speed and travelled distance, which in turn are used to provide velocity and position updates to the INS solution. When there is no pedaling motion, dynamics CDR is not used. However, to contribute to the INS solution in such scenarios, Non-Holonomic Constraints (NHC) are used. The proposed solution also includes an extension of CDR, multi-gear CDR (MG-CDR) that can handle bicycles with multiple gears. During GNSS availability, MG-CDR builds a group of models for different gear ratios. When GNSS signals are lost, the system runs a routine to detect the most likely gear ratio. The corresponding models, derived by matching or interpolation/extrapolation of the results of existing models, are used. In order to run CDR, MG-CDR and NHC, 3D misalignments are needed because both speed and travelled distance are in the bicycle frame not in the device frame (INS frame). The proposed system includes routines to calculate the 3D misalignments, whether in the presence or absence of GNSS. The proposed real-time navigation solution was tested extensively in a large number of trajectories collected by different users, on different bicycles, including both multi-gears and single-gear bicycles. The experiments included multiple different positions and orientations of the portable devices on the cyclists’ body. Different smartphones, tablets, and smartwatches were used in the experiments. The presented results demonstrate the capabilities and competitiveness of the proposed solution in the various real-life scenarios discussed.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».