Prediction Analysis of Optical Tracker Parameters using Machine Learning Approaches for efficient Head Tracking

TL;DR

This paper explores how machine learning can improve the prediction and analysis of optical tracker parameters for head tracking in simulators, considering environmental influences like lighting and distance.

Contribution

It introduces a machine learning approach to analyze optical tracker data, accounting for environmental factors affecting head tracking accuracy.

Findings

Optical tracker data varies with lighting and distance.

Environmental conditions significantly impact head tracking data.

Machine learning can help mitigate environmental effects.

Abstract

A head tracker is a crucial part of the head mounted display systems, as it tracks the head of the pilot in the plane/cockpit simulator. The operational flaws of head trackers are also dependent on different environmental conditions like different lighting conditions and stray light interference. In this letter, an optical tracker has been employed to gather the 6-DoF data of head movements under different environmental conditions. Also, the effect of different environmental conditions and variation in distance between the receiver and optical transmitter on the 6-DoF data was analyzed.