Controllers Technology

This page provides insights into controller technology, focusing on functionality, accuracy, and the inherent technological capabilities of Meta Quest controllers.

Meta Quest controllers are equipped with a variety of sensors, including an accelerometer and gyroscope, which continuously monitor their movement, orientation, and position. Additional sensors such as cameras and an array of infrared LEDs enhance the precision of tracking in 3D space. The Meta Quest headset’s (HMD) own sensors also play a crucial role in accurately determining the controllers’ location and movements. In addition to spatial tracking, the controllers feature buttons, triggers, and thumbsticks that enable users to interact with virtual content in diverse ways. These inputs are wirelessly transmitted to the Meta Quest headset and processed to produce corresponding actions.

Various factors can influence the accuracy of the controllers, including environmental variability. For further details on these challenges and the strategies used to mitigate them, refer to Controllers Best Practices. Understanding these factors is crucial for designing effective and immersive user experiences.

The technology behind the controllers has evolved over time, resulting in different capabilities and best practices. Our controllers can be categorized so far into two main groups:

6DOF controllers: These controllers have 6 degrees of freedom (6DOF), enabling both orientation and positional tracking (rotation, pitch, yaw, and movement along the x, y, and z axes). This allows controllers to function as virtual hands, interacting spatially with the virtual world.

These controllers shipped with PC-based HMD, such as Rift.

Self-tracked controllers: Self-tracked controllers are a type of 6DOF controller that uses onboard sensors to track their position and orientation in 3D space. They do not require external sensors or cameras to operate, making them more convenient and flexible than other types of controllers.

These controllers ship with standalone HMD, such as Meta Quest, and offer a more immersive and interactive experience for users.

Controller tracking can deliver sub-millimeter precision through the use of both classical computer vision and machine learning models. Machine perception cameras identify and triangulate the infrared LEDs on the controller, providing sub-pixel accuracy, while machine learning models are trained to estimate the pose of the controller when LEDs are occluded and in difficult lighting conditions. These systems leverage each other to maximize controller performance.

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