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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. [ABSTRACT FROM AUTHOR]

Pedestrian navigation with handheld sensors is still particularly complex. Pedestrian Dead Reckoning method is generally used, but the estimation of the walking direction remains problematic because the device's pointing direction does not always correspond to the walking direction. To overcome this difficulty, it is possible to use gait modeling based approaches. But, these methods suffer from sporadic erroneous estimates and their accumulation over time. The HEAD (smootH Estimation of wAlking Direction) filter uses WAISS and MAGYQ angular estimates as observations to correct the walking direction and to obtain more robust and smooth results. TDCP updates are applied to constrain the walking direction estimation error while pseudo‐ranges directly update the position. HEAD is tested by 5 subjects over 21 indoor/outdoor acquisitions (between 720 m and 1.3 km). A 54% improvement is achieved thanks to the fusion in texting mode. The median obtained angular error is 5.5∘ in texting mode and 12∘ in pocket mode. [ABSTRACT FROM AUTHOR]

Pedestrian Dead Reckoning algorithms are commonly used to assist pedestrian navigation with handheld sensors. The estimation of the walking direction remains an important source of positioning error of mobile phone users since this direction may be different from the device’s pointing direction. A better understanding of human walking gait has enabled to produce new algorithms to mitigate the impact of the way the device is held in hand. WAISS algorithm is one of them. It is based on the study of horizontal hand accelerations and their modeling using Gaussian Mixture Models (GMM). However, ongoing search for universal modeling of handheld device carrying mode defeats the varying nature of human gait. This paper investigates the impact of individual gait characteristics and their modeling to improve the estimation of the walking direction. Different models are learned for curved and straight lines and varying GMM are proposed to account for inter-individual gait variations. This results in a reduced walking direction error with a 8.1° mean error to the 90th percentile computed for 3 subjects over a 1.5 km indoor/outdoor walk. [ABSTRACT FROM AUTHOR]