An introduction to inertial navigation

By Oliver J. Woodman. 2007

1,043 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/dd785cbf1cea96eade72fff03eb3d30562feca5b

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Citations in the corpus

A Survey of Indoor Inertial Positioning Systems for Pedestrians (2013, methodology): “An INS uses triaxial accelerometers and gyroscopes to track orientation and position changes [47].”
Activity identification using body-mounted sensors—a review of classification techniques (2009, background): “Wu (2000) studied the horizontal and vertical velocity characteristics of falls using an optical motion capture system and showed that trunk vertical velocities associated with falls were 2-3 times those of everyday activities.”
Contact-aided invariant extended Kalman filtering for robot state estimation (2019, methodology): “This is done by replacing the potentially complicated process model with a relatively simple IMU integration model (also known as strapdown modeling) [78, 62, 82].”
AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles (2017, background): “Gyroscope and accelerometers constitute the core of the inertial measurement unit (IMU) [26].”
Using Inertial Sensors for Position and Orientation Estimation (2017, unknown): “pare the noise characteristics of inertial sensors. However, it only considers stationary conditions. In dynamic conditions, a large number of other error sources potentially come into play, see e.g. [147, 154]. These are for instance related to the fact that the sensors sample at discrete times. He”
Pedestrian localisation for indoor environments (2008, background): “The drift incurred by a high end MEMS IMU will typically exceed 100 metres after 1 minute of operation [19].”