Indoor location-based service is a hot research topic whose application market size is
expected to grow from 41 billion by 2022 and 58 billion by 2023. As a portable …

Pedestrian dead-reckoning (PDR) is a vital technique in pedestrian localization. Compared
with traditional PDR, learning-based inertial odometry has the advantages of smaller …

In this work, a novel hybrid neural network with temporal attention (HNNTA) is proposed for
inertial pedestrian localization. The HNNTA model employs the convolutional neural …

In this work, we propose A2DIO, a novel hybrid neural network model with a set of carefully
designed attention mechanisms for pose invariant inertial odometry. The key idea is to …

Low-cost inertial measurement units (IMUs) suffer from low sensitivity and high random walk
noise, which makes it challenging to use them directly for dead reckoning. Regular model …

Inertial localisation is an important technique as it enables ego-motion estimation in
conditions where external observers are unavailable. However, low-cost inertial sensors are …

An intelligent robot grasping system should be able to automatically grasp a variety of
objects that have never been seen, which requires accurate and efficient grasp pose …

This paper introduces rotation-equivariance as a self-supervisor to train inertial odometry
models. We demonstrate that the self-supervised scheme provides a powerful supervisory …

In this paper, we propose spatiotemporal co-attention hybrid neural network (SC-HNN), a
novel hybrid neural network model with both spatial and temporal attention mechanisms for …

Multisource navigation involves using multiple available sensors to achieve high-precision
location services, but navigation devices and data are susceptible to various environments …