Results 211 to 220 of about 16,587 (259)
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Low cost inertial orientation tracking with Kalman filter

Applied Mathematics and Computation, 2004
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Markus Haid, Jan Breitenbach
exaly   +3 more sources

IMULet

Proceedings of the 22nd International Workshop on Mobile Computing Systems and Applications, 2021
Inertial measurement units (IMUs) afford the problem of localisation unique advantages owing to their independence of costly deployment and calibration efforts. However, IMU models have traditionally suffered from excessive drifts that have limited their appeal and utility. Newer machine learning (ML) approaches can better model and compensate for such
Alloulah, Mohammed, Tuominen, Lauri
openaire   +1 more source

Closing the Gaps in Inertial Motion Tracking

Proceedings of the 24th Annual International Conference on Mobile Computing and Networking, 2018
A rich body of work has focused on motion tracking techniques using inertial sensors, namely accelerometers, gyroscopes, and magnetometers. Applications of these techniques are in indoor localization, gesture recognition, inventory tracking, vehicular motion, and many others.
Sheng Shen 0002   +2 more
openaire   +1 more source

Pedestrian tracking through inertial measurements

2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN), 2016
Most modern outdoor tracking systems utilize GPS technology. However, typical pedestrian environments tend to distort and block satellite signals, thereby making GPS communication systems unreliable. This is particularly true for indoor environments. An alternative pedestrian dead reckoning system based solely on inertial sensors is presented.
Kenneth Hili, Andrew Sammut
openaire   +1 more source

Use of inertial sensors to support video tracking

Computer Animation and Virtual Worlds, 2007
AbstractOne of the biggest obstacles to building effective augmented reality (AR) systems is the lack of accurate sensors that report the location of the user in an environment during arbitrary long periods of movements. In this paper, we present an effective hybrid approach that integrates inertial and vision‐based technologies. This work is motivated
Aron, Michael   +2 more
openaire   +2 more sources

Filling the gaps: Hybrid vision and inertial tracking

2013 IEEE International Symposium on Mixed and Augmented Reality (ISMAR), 2013
Existing head-tracking systems all suffer from various limitations, such as latency, cost, accuracy, or drift. I propose to address these limitations by using depth cameras and existing 3D reconstruction algorithms to simultaneously localize the camera position and build a map of the environment, providing stable and drift-free tracking. This method is
Ky Waegel, Frederick P. Brooks Jr.
openaire   +1 more source

Ambulatory inertial spinal tracking using constraints

Proceedings of the 9th International Conference on Body Area Networks, 2014
Wearable inertial sensor networks represent a well-known and meanwhile cheap solution for in-field motion capturing. However, the majority of existing approaches and products rely on simple stick figure models to approximate the human skeleton with only a few rigid segments and connecting joints.
Markus Miezal   +3 more
openaire   +1 more source

Limb motion tracking with inertial measurement units

2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2017
While the ability to perceive attitude in 3D trajectory of the movement is a basic concept of a motion capture, a 9-axis inertial measurement unit (IMU) device containing accelerometer, gyroscope and magnetometer is introduced as a challenge for motion tracking device.
Prabancoro Adhi Catur Widagdo   +2 more
openaire   +1 more source

VCIT - Visual Corrected Inertial Tracking

2017
Many smart systems depend on exact models of their environment. These are gained by tracking objects in their surroundings. When a highly precise system is only available for a small part of the environment, it can be enhanced with a second system to recover the unknown parts. This paper presents a method to recover loss of a precise (optical) tracking
Daniel Andrés López   +3 more
openaire   +1 more source

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