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3d Positioning & Sensor Fusion for Mixed Reality
MAS: Media Arts and Sciences
Cedric Honnet: firstname.lastname@example.org
From Neurosciences (rodent tracking) to Artistic Performances (Dancer tracking) or gaming (VR), accurate indoor 3d positioning has applications in almost every industry. In the context of an international collaboration, we miniaturized the HTC Vive Tracker and called it the "Hive Tracker": http://HiveTracker.github.io It performs the basic positioning, and we developed some theoretical parts of the calibration as well as the sensor fusion algorithm (Kalman Filter), but they still need to be verified, implemented, simulated, ported and characterized. If you enjoy Maths, Robotics, and Electronics, the experience that you will acquire in the project will reveal useful in many contexts and applications. More details about the project can be found here: https://docs.google.com/document/d/1pNqCdUkkI8hBsYokwHzWzamcNdn9_gCWBC4_MTGRRdQ/
Autonomous and motivated EECS student (seniors or experienced juniors) with a strong focus on math and/or signal processing. C/C++ is necessary and some knowledge about the Kalman Filter seems almost mandatory. [ TL;DR => Keywords: Kalman Filter, Embedded Systems, Human Computer Interaction, Mixed Reality ]