IMU - Revision history

RealEditor at 11:33, 22 July 2025

2025-07-22T11:33:06Z

← Older revision		Revision as of 11:33, 22 July 2025
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	~~{{see also\|Terms\|Technical Terms}}~~		An '''inertial measurement unit''' ('''IMU''') is an electronic [[sensor]] that measures and reports its specific force, angular rate, and sometimes the orientation of the body, using a combination of [[accelerometer]]s, [[gyroscope]]s, and often [[magnetometer]]s.<ref name="TDK_IMU_Overview">
	An '''~~Inertial Measurement Unit~~''' ('''IMU''') is an electronic [[sensor~~]] [[device~~]] that measures and reports ~~a body's~~ specific force, angular rate, and sometimes the orientation of the body, using a combination of [[accelerometer]]s, [[gyroscope]]s, and often [[magnetometer]]s.<ref name="TDK_IMU_Overview">
	TDK InvenSense. “What is an Inertial Measurement Unit (IMU)?”		TDK InvenSense. “What is an Inertial Measurement Unit (IMU)?”
	[https://invensense.tdk.com/products/motion-tracking/6-axis/ TDK InvenSense Website]. Accessed May 3, 2025.</ref> IMUs are fundamental components in [[virtual reality\|Virtual Reality (VR)]] and [[augmented reality\|Augmented Reality (AR)]] systems for tracking the orientation of [[Head-Mounted Display\|HMDs]] and [[Input Devices]] like controllers.		[https://invensense.tdk.com/products/motion-tracking/6-axis/ TDK InvenSense Website]. Accessed May 3, 2025.</ref> IMUs are fundamental components in [[virtual reality\|Virtual Reality (VR)]] and [[augmented reality\|Augmented Reality (AR)]] systems for tracking the orientation of [[Head-Mounted Display\|HMDs]] and [[Input Devices]] like controllers.

Xinreality at 07:47, 3 May 2025

2025-05-03T07:47:51Z

← Older revision		Revision as of 07:47, 3 May 2025
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	{{see also\|Terms\|Technical Terms}}		{{see also\|Terms\|Technical Terms}}
	An '''Inertial Measurement Unit''' ('''IMU''') is an electronic [[sensor]] [[device]] that measures and reports a body's specific force, angular rate, and sometimes the orientation of the body, using a combination of [[accelerometer]]s, [[gyroscope]]s, and often [[magnetometer]]s.<ref name="TDK_IMU_Overview"> ~~TDK InvenSense~~. ~~What is~~ an Inertial Measurement Unit (IMU)? [https://invensense.tdk.com/products/motion-tracking/6-axis/ ~~TDK InvenSense Website~~]. Accessed ~~October 26~~, ~~2023~~.</ref> IMUs are fundamental components in [[virtual reality\|Virtual Reality (VR)]] and [[augmented reality\|Augmented Reality (AR)]] systems for tracking the orientation of [[Head-Mounted Display\|HMDs]] and [[Input Devices]] like controllers.		An '''Inertial Measurement Unit''' ('''IMU''') is an electronic [[sensor]] [[device]] that measures and reports a body's specific force, angular rate, and sometimes the orientation of the body, using a combination of [[accelerometer]]s, [[gyroscope]]s, and often [[magnetometer]]s.<ref name="TDK_IMU_Overview">
			TDK InvenSense. “What is an Inertial Measurement Unit (IMU)?”
			[https://invensense.tdk.com/products/motion-tracking/6-axis/ TDK InvenSense Website]. Accessed May 3, 2025.</ref> IMUs are fundamental components in [[virtual reality\|Virtual Reality (VR)]] and [[augmented reality\|Augmented Reality (AR)]] systems for tracking the orientation of [[Head-Mounted Display\|HMDs]] and [[Input Devices]] like controllers.

	==Components and Function==		==Components and Function==
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	==Sensor Fusion==		==Sensor Fusion==
	Raw data from individual sensors can be noisy (for example accelerometers during fast movement) and inaccurate (for example gyroscopes drift). [[Sensor fusion]] algorithms, such as [[Kalman filter]]s or complementary filters, are essential.<ref name="Mahony_Filter"> Mahony, R., Hamel, T.~~, &~~ Pflimlin, ~~J. M. (2008)~~. ~~Nonlinear~~ Complementary Filters on the Special Orthogonal Group. ~~IEEE Transactions~~ on Automatic Control, 53(5)~~, 1203-1218~~. [https://doi.org/10.1109/TAC.2008.923738 ~~Abstract Link~~]</ref> These algorithms intelligently combine the data from the accelerometers, gyroscopes (and magnetometers, if present) to produce a single, more accurate, stable, and low-latency estimate of the device's orientation in real-time.		Raw data from individual sensors can be noisy (for example accelerometers during fast movement) and inaccurate (for example gyroscopes drift). [[Sensor fusion]] algorithms, such as [[Kalman filter]]s or complementary filters, are essential.<ref name="Mahony_Filter">
			Mahony, R.; Hamel, T.; Pflimlin, J‑M. “Nonlinear Complementary Filters on the Special Orthogonal Group.”
			IEEE Transactions on Automatic Control, 53 (5) (2008): 1203‑1218.
			[https://doi.org/10.1109/TAC.2008.923738 DOI link]</ref> These algorithms intelligently combine the data from the accelerometers, gyroscopes (and magnetometers, if present) to produce a single, more accurate, stable, and low-latency estimate of the device's orientation in real-time.

	== Role in VR/AR ==		== Role in VR/AR ==
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	* '''MPU-6050''': A popular low-cost 6-axis IMU (accelerometer + gyroscope) from InvenSense, used in hobbyist projects and early devices like the [[Oculus Rift DK1]].<ref name="MPU6050_Datasheet"> InvenSense Inc. MPU-6000 and MPU-6050 Product Specification Revision 3.4. [https://invensense.tdk.com/wp-content/uploads/2015/02/MPU-6000-Datasheet1.pdf Datasheet Link]</ref>		* '''MPU-6050''': A popular low-cost 6-axis IMU (accelerometer + gyroscope) from InvenSense, used in hobbyist projects and early devices like the [[Oculus Rift DK1]].<ref name="MPU6050_Datasheet"> InvenSense Inc. MPU-6000 and MPU-6050 Product Specification Revision 3.4. [https://invensense.tdk.com/wp-content/uploads/2015/02/MPU-6000-Datasheet1.pdf Datasheet Link]</ref>
	* '''MPU-9250''': An InvenSense 9-axis IMU (adds a magnetometer to the MPU-6xxx series capabilities).<ref name="MPU9250_Datasheet"> InvenSense Inc. MPU-9250 Product Specification Revision 1.1. [https://invensense.tdk.com/wp-content/uploads/2015/02/PS-MPU-9250A-01-v1.1.pdf Datasheet Link]</ref> Used in some dev kits and controllers.		* '''MPU-9250''': An InvenSense 9-axis IMU (adds a magnetometer to the MPU-6xxx series capabilities).<ref name="MPU9250_Datasheet"> InvenSense Inc. MPU-9250 Product Specification Revision 1.1. [https://invensense.tdk.com/wp-content/uploads/2015/02/PS-MPU-9250A-01-v1.1.pdf Datasheet Link]</ref> Used in some dev kits and controllers.
	* '''ICM-42688-P''': A high-performance 6-axis IMU from TDK InvenSense, known for its low noise and stability, used in the Meta [[Quest 2]] headset.<ref name="Quest2_Teardown_iFixit"> iFixit. ~~Meta Quest 2 Teardown~~. [https://www.ifixit.com/~~News~~/~~56892~~/~~oculus-quest-2-teardown-into-the-metaverse iFixit Teardown~~]. Accessed ~~October 26~~, ~~2023~~. ~~(Identifies the TDK ICM-42688-P chip)~~</ref>		* '''ICM-42688-P''': A high-performance 6-axis IMU from TDK InvenSense, known for its low noise and stability, used in the Meta [[Quest 2]] headset.<ref name="Quest2_Teardown_iFixit">
			iFixit. “Oculus Quest 2 Disassembly.”
			[https://www.ifixit.com/Guide/Oculus+Quest+2+Disassembly/139759 iFixit Repair Guide]. Accessed May 3, 2025.</ref>
	* '''BMI085/BMI270''': Bosch IMUs optimized for VR/AR applications, found in devices like the [[Valve Index]] controllers.<ref>Nield, David. "How VR Headsets Are Getting Better Through Improved Tracking." TechRadar, 2022.</ref>		* '''BMI085/BMI270''': Bosch IMUs optimized for VR/AR applications, found in devices like the [[Valve Index]] controllers.<ref>Nield, David. "How VR Headsets Are Getting Better Through Improved Tracking." TechRadar, 2022.</ref>
	* '''LSM6DSO/LSM6DSOX''': STMicroelectronics 6-axis IMUs used in various HMDs and AR glasses, including the [[HoloLens 2]].		* '''LSM6DSO/LSM6DSOX''': STMicroelectronics 6-axis IMUs used in various HMDs and AR glasses, including the [[HoloLens 2]].

Xinreality at 07:46, 3 May 2025

2025-05-03T07:46:15Z

← Older revision		Revision as of 07:46, 3 May 2025
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	{{see also\|Terms\|Technical Terms}}		{{see also\|Terms\|Technical Terms}}
	An '''Inertial Measurement Unit''' ('''IMU''') is an electronic [[sensor]] [[device]] that measures and reports a body's specific force, angular rate, and sometimes the orientation of the body, using a combination of [[accelerometer]]s, [[gyroscope]]s, and often [[magnetometer]]s.<ref name="TDK_IMU_Overview"> TDK InvenSense. What is an Inertial Measurement Unit (IMU)? [https://invensense.tdk.com/~~technology~~/motion/~~imu~~/ TDK InvenSense Website]. Accessed October 26, 2023.</ref> IMUs are fundamental components in [[virtual reality\|Virtual Reality (VR)]] and [[augmented reality\|Augmented Reality (AR)]] systems for tracking the orientation of [[Head-Mounted Display\|HMDs]] and [[Input Devices]] like controllers.		An '''Inertial Measurement Unit''' ('''IMU''') is an electronic [[sensor]] [[device]] that measures and reports a body's specific force, angular rate, and sometimes the orientation of the body, using a combination of [[accelerometer]]s, [[gyroscope]]s, and often [[magnetometer]]s.<ref name="TDK_IMU_Overview"> TDK InvenSense. What is an Inertial Measurement Unit (IMU)? [https://invensense.tdk.com/products/motion-tracking/6-axis/ TDK InvenSense Website]. Accessed October 26, 2023.</ref> IMUs are fundamental components in [[virtual reality\|Virtual Reality (VR)]] and [[augmented reality\|Augmented Reality (AR)]] systems for tracking the orientation of [[Head-Mounted Display\|HMDs]] and [[Input Devices]] like controllers.

	==Components and Function==		==Components and Function==
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	==Sensor Fusion==		==Sensor Fusion==
	Raw data from individual sensors can be noisy (for example accelerometers during fast movement) and inaccurate (for example gyroscopes drift). [[Sensor fusion]] algorithms, such as [[Kalman filter]]s or complementary filters, are essential.<ref name="Mahony_Filter"> Mahony, R., Hamel, T., & Pflimlin, J. M. (2008). Nonlinear Complementary Filters on the Special Orthogonal Group. IEEE Transactions on Automatic Control, 53(5), 1203-1218. [https://~~ieeexplore.ieee~~.org/~~document~~/~~4532514~~ Abstract Link]</ref> These algorithms intelligently combine the data from the accelerometers, gyroscopes (and magnetometers, if present) to produce a single, more accurate, stable, and low-latency estimate of the device's orientation in real-time.		Raw data from individual sensors can be noisy (for example accelerometers during fast movement) and inaccurate (for example gyroscopes drift). [[Sensor fusion]] algorithms, such as [[Kalman filter]]s or complementary filters, are essential.<ref name="Mahony_Filter"> Mahony, R., Hamel, T., & Pflimlin, J. M. (2008). Nonlinear Complementary Filters on the Special Orthogonal Group. IEEE Transactions on Automatic Control, 53(5), 1203-1218. [https://doi.org/10.1109/TAC.2008.923738 Abstract Link]</ref> These algorithms intelligently combine the data from the accelerometers, gyroscopes (and magnetometers, if present) to produce a single, more accurate, stable, and low-latency estimate of the device's orientation in real-time.

	== Role in VR/AR ==		== Role in VR/AR ==
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	* '''MPU-6050''': A popular low-cost 6-axis IMU (accelerometer + gyroscope) from InvenSense, used in hobbyist projects and early devices like the [[Oculus Rift DK1]].<ref name="MPU6050_Datasheet"> InvenSense Inc. MPU-6000 and MPU-6050 Product Specification Revision 3.4. [https://invensense.tdk.com/wp-content/uploads/2015/02/MPU-6000-Datasheet1.pdf Datasheet Link]</ref>		* '''MPU-6050''': A popular low-cost 6-axis IMU (accelerometer + gyroscope) from InvenSense, used in hobbyist projects and early devices like the [[Oculus Rift DK1]].<ref name="MPU6050_Datasheet"> InvenSense Inc. MPU-6000 and MPU-6050 Product Specification Revision 3.4. [https://invensense.tdk.com/wp-content/uploads/2015/02/MPU-6000-Datasheet1.pdf Datasheet Link]</ref>
	* '''MPU-9250''': An InvenSense 9-axis IMU (adds a magnetometer to the MPU-6xxx series capabilities).<ref name="MPU9250_Datasheet"> InvenSense Inc. MPU-9250 Product Specification Revision 1.1. [https://invensense.tdk.com/wp-content/uploads/2015/02/PS-MPU-9250A-01-v1.1.pdf Datasheet Link]</ref> Used in some dev kits and controllers.		* '''MPU-9250''': An InvenSense 9-axis IMU (adds a magnetometer to the MPU-6xxx series capabilities).<ref name="MPU9250_Datasheet"> InvenSense Inc. MPU-9250 Product Specification Revision 1.1. [https://invensense.tdk.com/wp-content/uploads/2015/02/PS-MPU-9250A-01-v1.1.pdf Datasheet Link]</ref> Used in some dev kits and controllers.
	* '''ICM-42688-P''': A high-performance 6-axis IMU from TDK InvenSense, known for its low noise and stability, used in the Meta [[Quest 2]] headset.<ref name="Quest2_Teardown_iFixit"> iFixit. Meta Quest 2 Teardown. [https://www.ifixit.com/~~Teardown~~/~~Oculus+Quest+~~2~~+Teardown/137613~~ iFixit Teardown]. Accessed October 26, 2023. (Identifies the TDK ICM-42688-P chip)</ref>		* '''ICM-42688-P''': A high-performance 6-axis IMU from TDK InvenSense, known for its low noise and stability, used in the Meta [[Quest 2]] headset.<ref name="Quest2_Teardown_iFixit"> iFixit. Meta Quest 2 Teardown. [https://www.ifixit.com/News/56892/oculus-quest-2-teardown-into-the-metaverse iFixit Teardown]. Accessed October 26, 2023. (Identifies the TDK ICM-42688-P chip)</ref>
	* '''BMI085/BMI270''': Bosch IMUs optimized for VR/AR applications, found in devices like the [[Valve Index]] controllers.<ref>Nield, David. "How VR Headsets Are Getting Better Through Improved Tracking." TechRadar, 2022.</ref>		* '''BMI085/BMI270''': Bosch IMUs optimized for VR/AR applications, found in devices like the [[Valve Index]] controllers.<ref>Nield, David. "How VR Headsets Are Getting Better Through Improved Tracking." TechRadar, 2022.</ref>
	* '''LSM6DSO/LSM6DSOX''': STMicroelectronics 6-axis IMUs used in various HMDs and AR glasses, including the [[HoloLens 2]].		* '''LSM6DSO/LSM6DSOX''': STMicroelectronics 6-axis IMUs used in various HMDs and AR glasses, including the [[HoloLens 2]].

Xinreality: Undo revision 34987 by Xinreality (talk)

2025-05-03T07:40:52Z

Undo revision 34987 by Xinreality (talk)

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Xinreality at 07:40, 3 May 2025

2025-05-03T07:40:44Z

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Xinreality at 07:56, 29 April 2025

2025-04-29T07:56:02Z

← Older revision		Revision as of 07:56, 29 April 2025
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	==Sensor Fusion==		==Sensor Fusion==
	Raw data from individual sensors can be noisy (for example accelerometers during fast movement) and inaccurate (for example gyroscopes drift). [[Sensor fusion]] algorithms, such as [[Kalman filter]]s or complementary filters, are essential.<ref name="Mahony_Filter"> Mahony, R., Hamel, T., & Pflimlin, J. M. (2008). Nonlinear Complementary Filters on the Special Orthogonal Group. IEEE Transactions on Automatic Control, 53(5), ~~1203–1218~~. [https://ieeexplore.ieee.org/document/4532514 Abstract Link]</ref> These algorithms intelligently combine the data from the accelerometers, gyroscopes (and magnetometers, if present) to produce a single, more accurate, stable, and low-latency estimate of the device's orientation in real-time.		Raw data from individual sensors can be noisy (for example accelerometers during fast movement) and inaccurate (for example gyroscopes drift). [[Sensor fusion]] algorithms, such as [[Kalman filter]]s or complementary filters, are essential.<ref name="Mahony_Filter"> Mahony, R., Hamel, T., & Pflimlin, J. M. (2008). Nonlinear Complementary Filters on the Special Orthogonal Group. IEEE Transactions on Automatic Control, 53(5), 1203-1218. [https://ieeexplore.ieee.org/document/4532514 Abstract Link]</ref> These algorithms intelligently combine the data from the accelerometers, gyroscopes (and magnetometers, if present) to produce a single, more accurate, stable, and low-latency estimate of the device's orientation in real-time.

	== Role in VR/AR ==		== Role in VR/AR ==
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	===6DoF Tracking Systems===		===6DoF Tracking Systems===
	For full [[6DoF]] tracking (which includes [[positional tracking]] – translation along X, Y, and Z axes), IMU data is combined via sensor fusion with data from other tracking systems. These can include:		For full [[6DoF]] tracking (which includes [[positional tracking]] translation along X, Y, and Z axes), IMU data is combined via sensor fusion with data from other tracking systems. These can include:
	* [[Inside-out tracking]]: Cameras on the HMD observe the external environment.		* [[Inside-out tracking]]: Cameras on the HMD observe the external environment.
	* [[Outside-in tracking]]: External sensors (like cameras or [[lighthouse tracking\|base stations]]) observe markers on the HMD and controllers.		* [[Outside-in tracking]]: External sensors (like cameras or [[lighthouse tracking\|base stations]]) observe markers on the HMD and controllers.

Xinreality: Text replacement - "e.g.," to "for example"

2025-04-29T04:22:59Z

Text replacement - "e.g.," to "for example"

← Older revision		Revision as of 04:22, 29 April 2025
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	==Sensor Fusion==		==Sensor Fusion==
	Raw data from individual sensors can be noisy (~~e.g.,~~ accelerometers during fast movement) and inaccurate (~~e.g.,~~ gyroscopes drift). [[Sensor fusion]] algorithms, such as [[Kalman filter]]s or complementary filters, are essential.<ref name="Mahony_Filter"> Mahony, R., Hamel, T., & Pflimlin, J. M. (2008). Nonlinear Complementary Filters on the Special Orthogonal Group. IEEE Transactions on Automatic Control, 53(5), 1203–1218. [https://ieeexplore.ieee.org/document/4532514 Abstract Link]</ref> These algorithms intelligently combine the data from the accelerometers, gyroscopes (and magnetometers, if present) to produce a single, more accurate, stable, and low-latency estimate of the device's orientation in real-time.		Raw data from individual sensors can be noisy (for example accelerometers during fast movement) and inaccurate (for example gyroscopes drift). [[Sensor fusion]] algorithms, such as [[Kalman filter]]s or complementary filters, are essential.<ref name="Mahony_Filter"> Mahony, R., Hamel, T., & Pflimlin, J. M. (2008). Nonlinear Complementary Filters on the Special Orthogonal Group. IEEE Transactions on Automatic Control, 53(5), 1203–1218. [https://ieeexplore.ieee.org/document/4532514 Abstract Link]</ref> These algorithms intelligently combine the data from the accelerometers, gyroscopes (and magnetometers, if present) to produce a single, more accurate, stable, and low-latency estimate of the device's orientation in real-time.

	== Role in VR/AR ==		== Role in VR/AR ==

Xinreality at 06:56, 24 April 2025

2025-04-24T06:56:40Z

← Older revision		Revision as of 06:56, 24 April 2025
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	{{~~Infobox technology~~		{{see also\|Terms\|Technical Terms}}
	\| ~~name = IMU (Inertial Measurement Unit)~~
	\| ~~image = IMU_diagram.png~~
	~~\| caption = Diagram illustrating the typical axes measured by a 9-axis IMU.~~
	}}

	An '''Inertial Measurement Unit''' ('''IMU''') is an electronic [[sensor]] [[device]] that measures and reports a body's specific force, angular rate, and sometimes the orientation of the body, using a combination of [[accelerometer]]s, [[gyroscope]]s, and often [[magnetometer]]s.<ref name="TDK_IMU_Overview"> TDK InvenSense. What is an Inertial Measurement Unit (IMU)? [https://invensense.tdk.com/technology/motion/imu/ TDK InvenSense Website]. Accessed October 26, 2023.</ref> IMUs are fundamental components in [[virtual reality\|Virtual Reality (VR)]] and [[augmented reality\|Augmented Reality (AR)]] systems for tracking the orientation of [[Head-Mounted Display\|HMDs]] and [[Input Devices]] like controllers.		An '''Inertial Measurement Unit''' ('''IMU''') is an electronic [[sensor]] [[device]] that measures and reports a body's specific force, angular rate, and sometimes the orientation of the body, using a combination of [[accelerometer]]s, [[gyroscope]]s, and often [[magnetometer]]s.<ref name="TDK_IMU_Overview"> TDK InvenSense. What is an Inertial Measurement Unit (IMU)? [https://invensense.tdk.com/technology/motion/imu/ TDK InvenSense Website]. Accessed October 26, 2023.</ref> IMUs are fundamental components in [[virtual reality\|Virtual Reality (VR)]] and [[augmented reality\|Augmented Reality (AR)]] systems for tracking the orientation of [[Head-Mounted Display\|HMDs]] and [[Input Devices]] like controllers.

	== Components and Function ==		==Components and Function==
	A typical IMU integrates multiple sensor types onto a microchip:		A typical IMU integrates multiple sensor types onto a microchip:

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	When an IMU includes all three sensors (accelerometer, gyroscope, and magnetometer), it is sometimes referred to as a 9-axis IMU or a [[MARG]] (Magnetic, Angular Rate, and Gravity) sensor.<ref>Madgwick, Sebastian OH, Andrew JL Harrison, and Ravi Vaidyanathan. "Estimation of IMU and MARG orientation using a gradient descent algorithm." IEEE international conference on rehabilitation robotics. IEEE, 2011.</ref>		When an IMU includes all three sensors (accelerometer, gyroscope, and magnetometer), it is sometimes referred to as a 9-axis IMU or a [[MARG]] (Magnetic, Angular Rate, and Gravity) sensor.<ref>Madgwick, Sebastian OH, Andrew JL Harrison, and Ravi Vaidyanathan. "Estimation of IMU and MARG orientation using a gradient descent algorithm." IEEE international conference on rehabilitation robotics. IEEE, 2011.</ref>

	== Sensor Fusion ==		==Sensor Fusion==
	Raw data from individual sensors can be noisy (e.g., accelerometers during fast movement) and inaccurate (e.g., gyroscopes drift). [[Sensor fusion]] algorithms, such as [[Kalman filter]]s or complementary filters, are essential.<ref name="Mahony_Filter"> Mahony, R., Hamel, T., & Pflimlin, J. M. (2008). Nonlinear Complementary Filters on the Special Orthogonal Group. IEEE Transactions on Automatic Control, 53(5), 1203–1218. [https://ieeexplore.ieee.org/document/4532514 Abstract Link]</ref> These algorithms intelligently combine the data from the accelerometers, gyroscopes (and magnetometers, if present) to produce a single, more accurate, stable, and low-latency estimate of the device's orientation in real-time.		Raw data from individual sensors can be noisy (e.g., accelerometers during fast movement) and inaccurate (e.g., gyroscopes drift). [[Sensor fusion]] algorithms, such as [[Kalman filter]]s or complementary filters, are essential.<ref name="Mahony_Filter"> Mahony, R., Hamel, T., & Pflimlin, J. M. (2008). Nonlinear Complementary Filters on the Special Orthogonal Group. IEEE Transactions on Automatic Control, 53(5), 1203–1218. [https://ieeexplore.ieee.org/document/4532514 Abstract Link]</ref> These algorithms intelligently combine the data from the accelerometers, gyroscopes (and magnetometers, if present) to produce a single, more accurate, stable, and low-latency estimate of the device's orientation in real-time.

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	IMUs provide the rapid [[orientation tracking]] needed to update the virtual view in sync with the user's head movements. This low latency is critical for user comfort. The typical update rate of modern IMUs used in VR headsets is between 500Hz to 1000Hz, much faster than most visual tracking systems can achieve alone.<ref>Niehorster, Diederick C., Li Li, and Markus Lappe. "The accuracy and precision of position and orientation tracking in the HTC Vive virtual reality system for scientific research." i-Perception 8.3 (2017).</ref>		IMUs provide the rapid [[orientation tracking]] needed to update the virtual view in sync with the user's head movements. This low latency is critical for user comfort. The typical update rate of modern IMUs used in VR headsets is between 500Hz to 1000Hz, much faster than most visual tracking systems can achieve alone.<ref>Niehorster, Diederick C., Li Li, and Markus Lappe. "The accuracy and precision of position and orientation tracking in the HTC Vive virtual reality system for scientific research." i-Perception 8.3 (2017).</ref>

	=== 3 Degrees of Freedom (DoF) ===		===3 Degrees of Freedom (DoF)===
	An IMU inherently provides [[Degrees of Freedom\|3 DoF tracking]], measuring orientation changes (pitch, yaw, roll). This is sufficient for basic VR experiences like 360-degree video viewing on mobile VR headsets where the user's physical position in the room is not tracked.		An IMU inherently provides [[Degrees of Freedom\|3 DoF tracking]], measuring orientation changes (pitch, yaw, roll). This is sufficient for basic VR experiences like 360-degree video viewing on mobile VR headsets where the user's physical position in the room is not tracked.

	=== 6DoF Tracking Systems ===		===6DoF Tracking Systems===
	For full [[6DoF]] tracking (which includes [[positional tracking]] – translation along X, Y, and Z axes), IMU data is combined via sensor fusion with data from other tracking systems. These can include:		For full [[6DoF]] tracking (which includes [[positional tracking]] – translation along X, Y, and Z axes), IMU data is combined via sensor fusion with data from other tracking systems. These can include:
	* [[Inside-out tracking]]: Cameras on the HMD observe the external environment.		* [[Inside-out tracking]]: Cameras on the HMD observe the external environment.
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	* [[Zero velocity updates]] (ZUPTs) during periods of detected stillness to reset velocity error accumulation<ref>Cadena, Cesar, et al. "Past, present, and future of simultaneous localization and mapping: Toward the robust-perception age." IEEE Transactions on robotics 32.6 (2016): 1309-1332.</ref>		* [[Zero velocity updates]] (ZUPTs) during periods of detected stillness to reset velocity error accumulation<ref>Cadena, Cesar, et al. "Past, present, and future of simultaneous localization and mapping: Toward the robust-perception age." IEEE Transactions on robotics 32.6 (2016): 1309-1332.</ref>

	== IMU Specifications for VR/AR ==		==IMU Specifications for VR/AR==
	For optimal performance in VR/AR applications, IMUs typically require:		For optimal performance in VR/AR applications, IMUs typically require:

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	* Integrated processing capabilities (sometimes including basic sensor fusion)<ref>Angelini, Lee, et al. "Understanding sensors: prioritizations for selecting sensors in mobile VR applications." Internet Research (2022).</ref>		* Integrated processing capabilities (sometimes including basic sensor fusion)<ref>Angelini, Lee, et al. "Understanding sensors: prioritizations for selecting sensors in mobile VR applications." Internet Research (2022).</ref>

	== Future Developments ==		==Future Developments==
	Next-generation IMUs for VR/AR are focusing on:		Next-generation IMUs for VR/AR are focusing on:

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	* Further reduction in sensor noise and drift characteristics<ref>Adams, Michael D. "MEMS IMU Navigation with Model Based Dead-Reckoning and One-Way-Travel-Time Acoustic Measurements." IEEE Journal of Oceanic Engineering (2023).</ref>		* Further reduction in sensor noise and drift characteristics<ref>Adams, Michael D. "MEMS IMU Navigation with Model Based Dead-Reckoning and One-Way-Travel-Time Acoustic Measurements." IEEE Journal of Oceanic Engineering (2023).</ref>

	== Key IMU Manufacturers ==		==Key IMU Manufacturers==
	Several companies manufacture IMUs used in consumer electronics, including VR/AR devices:		Several companies manufacture IMUs used in consumer electronics, including VR/AR devices:
	* [[TDK]] [[Invensense]]<ref name="TDK_Homepage"> TDK InvenSense - Motion Sensors [https://invensense.tdk.com/products/motion-tracking/ TDK InvenSense Website]. Accessed October 26, 2023.</ref> - Major provider for consumer electronics.		* [[TDK]] [[Invensense]]<ref name="TDK_Homepage"> TDK InvenSense - Motion Sensors [https://invensense.tdk.com/products/motion-tracking/ TDK InvenSense Website]. Accessed October 26, 2023.</ref> - Major provider for consumer electronics.
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	* [[Xsens]] - Specializes in high-precision motion tracking modules often incorporating IMUs.<ref>Yole Développement. "MEMS & Sensors for Wearables Report." 2023.</ref>		* [[Xsens]] - Specializes in high-precision motion tracking modules often incorporating IMUs.<ref>Yole Développement. "MEMS & Sensors for Wearables Report." 2023.</ref>

	== Notable IMU Models in VR/AR ==		==Notable IMU Models in VR/AR==
	* '''MPU-6050''': A popular low-cost 6-axis IMU (accelerometer + gyroscope) from InvenSense, used in hobbyist projects and early devices like the [[Oculus Rift DK1]].<ref name="MPU6050_Datasheet"> InvenSense Inc. MPU-6000 and MPU-6050 Product Specification Revision 3.4. [https://invensense.tdk.com/wp-content/uploads/2015/02/MPU-6000-Datasheet1.pdf Datasheet Link]</ref>		* '''MPU-6050''': A popular low-cost 6-axis IMU (accelerometer + gyroscope) from InvenSense, used in hobbyist projects and early devices like the [[Oculus Rift DK1]].<ref name="MPU6050_Datasheet"> InvenSense Inc. MPU-6000 and MPU-6050 Product Specification Revision 3.4. [https://invensense.tdk.com/wp-content/uploads/2015/02/MPU-6000-Datasheet1.pdf Datasheet Link]</ref>
	* '''MPU-9250''': An InvenSense 9-axis IMU (adds a magnetometer to the MPU-6xxx series capabilities).<ref name="MPU9250_Datasheet"> InvenSense Inc. MPU-9250 Product Specification Revision 1.1. [https://invensense.tdk.com/wp-content/uploads/2015/02/PS-MPU-9250A-01-v1.1.pdf Datasheet Link]</ref> Used in some dev kits and controllers.		* '''MPU-9250''': An InvenSense 9-axis IMU (adds a magnetometer to the MPU-6xxx series capabilities).<ref name="MPU9250_Datasheet"> InvenSense Inc. MPU-9250 Product Specification Revision 1.1. [https://invensense.tdk.com/wp-content/uploads/2015/02/PS-MPU-9250A-01-v1.1.pdf Datasheet Link]</ref> Used in some dev kits and controllers.
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	* '''LSM6DSO/LSM6DSOX''': STMicroelectronics 6-axis IMUs used in various HMDs and AR glasses, including the [[HoloLens 2]].		* '''LSM6DSO/LSM6DSOX''': STMicroelectronics 6-axis IMUs used in various HMDs and AR glasses, including the [[HoloLens 2]].

	== References ==		==References==
	<references />		<references />

	[[Category:Terms]] [[Category:Technical Terms]] [[Category:Tracking]] [[Category:Tracking Technology]] [[Category:Hardware]]		[[Category:Terms]] [[Category:Technical Terms]] [[Category:Tracking]] [[Category:Tracking Technology]] [[Category:Hardware]]

Xinreality at 06:33, 24 April 2025

2025-04-24T06:33:50Z

Show changes

Xinreality at 05:43, 24 April 2025

2025-04-24T05:43:05Z

← Older revision		Revision as of 05:43, 24 April 2025
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	*MPU-9250		*MPU-9250
	*icm-42688-p - used in the [[Quest 2]] headset		*icm-42688-p - used in the [[Quest 2]] headset
	[[Category:Terms]]
			==References==
			<references />


			[[Category:Terms]] [[Category:Technical Terms]]