陀螺仪LSM6DSV16X与AI集成(2)-姿态解算资源-CSDN文库

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姿态解算

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陀螺仪LSM6DSV16X与AI集成(2)-姿态解算（185个子文件）

project1_lsm6dsv16x_read_data_polling.uvguix.a8456 91KB

project1_lsm6dsv16x_read_data_polling.axf 437KB

lsm6dsv16x_reg.c 296KB

stm32wbxx_hal_tim.c 255KB

stm32wbxx_hal_i2c.c 244KB

stm32wbxx_hal_uart.c 163KB

stm32wbxx_hal_tim_ex.c 97KB

stm32wbxx_hal_rcc_ex.c 75KB

stm32wbxx_hal_rcc.c 65KB

stm32wbxx_hal_pwr_ex.c 47KB

stm32wbxx_hal_flash_ex.c 43KB

stm32wbxx_ll_rcc.c 41KB

stm32wbxx_hal_uart_ex.c 37KB

stm32wbxx_hal_dma.c 35KB

stm32wbxx_hal_pwr.c 29KB

stm32wbxx_hal.c 26KB

stm32wbxx_hal_flash.c 23KB

stm32wbxx_hal_gpio.c 20KB

stm32wbxx_hal_exti.c 18KB

stm32wbxx_hal_cortex.c 17KB

main.c 16KB

system_stm32wbxx.c 14KB

stm32wbxx_hal_hsem.c 12KB

stm32wbxx_hal_i2c_ex.c 11KB

stm32wbxx_hal_dma_ex.c 11KB

stm32wbxx_it.c 5KB

usart.c 4KB

i2c.c 4KB

gpio.c 2KB

stm32wbxx_hal_msp.c 2KB

memorymap.c 1KB

四旋翼无人机姿态解算与控制方法研究_廖坤男.caj 4.5MB

多传感器数据融合的四轴飞行器姿态角解算_唐震宇.caj 981KB

基于STM32的四旋翼飞行器的姿态最优估计研究_何川 (1).caj 766KB

main.d 3KB

stm32wbxx_it.d 2KB

stm32wbxx_hal_flash_ex.d 2KB

stm32wbxx_hal_uart_ex.d 2KB

stm32wbxx_hal_tim_ex.d 2KB

stm32wbxx_hal_i2c_ex.d 2KB

stm32wbxx_hal_dma_ex.d 2KB

stm32wbxx_hal_pwr_ex.d 2KB

stm32wbxx_hal_rcc_ex.d 2KB

stm32wbxx_hal_cortex.d 2KB

stm32wbxx_hal_flash.d 2KB

memorymap.d 2KB

stm32wbxx_hal_uart.d 2KB

stm32wbxx_hal_exti.d 2KB

stm32wbxx_hal_gpio.d 2KB

stm32wbxx_hal_hsem.d 2KB

stm32wbxx_hal_pwr.d 2KB

stm32wbxx_hal_dma.d 2KB

stm32wbxx_hal_rcc.d 2KB

stm32wbxx_hal_tim.d 2KB

stm32wbxx_hal_i2c.d 2KB

stm32wbxx_hal_msp.d 2KB

stm32wbxx_hal.d 2KB

usart.d 2KB

gpio.d 2KB

i2c.d 2KB

system_stm32wbxx.d 2KB

lsm6dsv16x_reg.d 277B

stm32wbxx_ll_rcc.d 119B

project1_lsm6dsv16x_read_data_polling_project1_lsm6dsv16x_read_data_polling.dep 108KB

stm32wb55xx.h 1.05MB

stm32_hal_legacy.h 234KB

stm32wbxx_hal_rcc.h 187KB

stm32wbxx_ll_rcc.h 176KB

stm32wbxx_ll_usart.h 175KB

lsm6dsv16x_reg.h 174KB

core_armv81mml.h 168KB

core_cm35p.h 164KB

core_cm33.h 164KB

core_armv8mml.h 158KB

core_cm7.h 148KB

stm32wbxx_hal_tim.h 142KB

core_cm4.h 120KB

core_cm3.h 109KB

core_sc300.h 108KB

core_cm23.h 102KB

stm32wbxx_ll_bus.h 98KB

stm32wbxx_ll_system.h 97KB

stm32wbxx_ll_pwr.h 97KB

core_armv8mbl.h 96KB

stm32wbxx_ll_lpuart.h 96KB

stm32wbxx_hal_uart.h 90KB

stm32wbxx_ll_dma.h 87KB

stm32wbxx_ll_i2c.h 84KB

stm32wbxx_ll_dmamux.h 81KB

stm32wbxx_hal_rcc_ex.h 75KB

stm32wbxx_ll_exti.h 65KB

cmsis_gcc.h 63KB

stm32wbxx_hal_flash.h 58KB

cmsis_armclang_ltm.h 56KB

core_cm0plus.h 49KB

core_sc000.h 46KB

cmsis_armclang.h 46KB

stm32wbxx_hal_pwr_ex.h 45KB

core_cm1.h 43KB

core_cm0.h 41KB

共 185 条

Features

• Triple-channel architecture for UI, EIS, and OIS data processing

• "Always-on" experience with low power consumption for both accelerometer

and gyroscope

• Smart FIFO up to 4.5 KB

• Android compliant

• ±2/±4/±8/±16 g full scale

• ±125/±250/±500/±1000/±2000/±4000 dps full scale

• SPI / I²C & MIPI I3C

v1.1 serial interface with main processor data

synchronization

• Auxiliary SPI for OIS data output for gyroscope and accelerometer

•

OIS configurable from aux. SPI, primary interface (SPI / I²C & MIPI I3C

v1.1)

• EIS dedicated channel on primary interface with dedicated filtering

• Advanced pedometer, step detector, and step counter

• Significant motion detection, tilt detection

• Standard interrupts: free-fall, wake-up, 6D/4D orientation, click and double

click

• Programmable finite state machine for accelerometer, gyroscope, and external

sensor data processing with high rate @ 960 Hz

• Machine learning core with exportable features and filters for AI applications

• Embedded adaptive self-configuration (ASC)

• Embedded Qvar (electrostatic sensor) for user interface functions (tap, double

tap, triple tap, long press, L/R – R/L swipe)

• Embedded analog hub for ADC and processing analog input data

• Embedded sensor fusion low-power algorithm

• Embedded temperature sensor

• Analog supply voltage: 1.71 V to 3.6 V

• Independent IO supply (extended range: 1.08 V to 3.6 V)

• Power consumption: 0.65 mA in combo high-performance mode

• Compact footprint: 2.5 mm x 3 mm x 0.83 mm

• ECOPACK and RoHS compliant

Applications

• Motion tracking and gesture detection, augmented reality (AR) / virtual reality

(VR) / mixed reality (MR) applications & metaverse applications

• Wearables

• Indoor navigation

• IoT and connected devices

• Smartphones and handheld devices

• EIS and OIS for camera applications

• Vibration monitoring and compensation

LSM6DSV16X

LGA-14L

(2.5 x 3.0 x 0.83 mm) typ.

Product status link

LSM6DSV16X

Product summary

Order code LSM6DSV16X LSM6DSV16XTR

Temp. range

[°C]

-40 to +85

Package

LGA-14L

(2.5 x 3.0 x 0.83 mm)

Packing Tray Tape and reel

Product resources

AN5763 (device application note)

AN5882 (finite state machine)

AN5804 (machine learning core)

AN5755 (Qvar sensing)

TN0018 (design and soldering)

Product label

6-axis inertial measurement unit (IMU) and AI sensor

with embedded sensor fusion, Qvar for high-end applications

LSM6DSV16X

Datasheet

DS13510 - Rev 4 - May 2023

For further information contact your local STMicroelectronics sales office.

www.st.com

Description

The LSM6DSV16X is a high-performance, low-power 6-axis small IMU, featuring a 3-axis digital accelerometer

and a 3-axis digital gyroscope, that offers the best IMU sensor with a triple-channel architecture for processing

acceleration and angular rate data on three separate channels (user interface, OIS, and EIS) with dedicated

configuration, processing, and filtering.

The LSM6DSV16X enables processes in edge computing, leveraging embedded advanced dedicated features

such as a finite state machine (FSM) for configurable motion tracking and a machine learning core (MLC) for

context awareness with exportable AI features for IoT applications.

The LSM6DSV16X supports the adaptive self-configuration (ASC) feature, which allows the FSM to automatically

reconfigure the device in real time based on the detection of a specific motion pattern or based on the output of a

specific decision tree configured in the MLC, without any intervention from the host processor.

The LSM6DSV16X embeds Qvar (electric charge variation detection) for user interface functions like tap, double

tap, triple tap, long press, or L/R – R/L swipe.

The LSM6DSV16X embeds an analog hub able to connect an external analog input and convert it to a digital

signal for processing.

LSM6DSV16X

DS13510 - Rev 4

page 2/198

1 Overview

The LSM6DSV16X is a system-in-package featuring a high-performance 3-axis digital accelerometer and 3-axis

digital gyroscope.

The LSM6DSV16X delivers best-in-class motion sensing that can detect orientation and gestures in order to

empower application developers and consumers with features and capabilities that are more sophisticated than

simply orienting their devices to portrait and landscape mode.

The event-detection interrupts enable efficient and reliable motion tracking and context awareness, implementing

hardware recognition of free-fall events, 6D orientation, click and double-click sensing, activity or inactivity,

stationary/motion detection and wake-up events. Machine learning and finite state machine processing allow

moving some algorithms from the application processor to the LSM6DSV16X sensor, enabling consistent

reduction of power consumption.

The LSM6DSV16X supports the main OS requirements, offering real, virtual, and batch mode sensors. In

addition, the LSM6DSV16X can efficiently run the sensor-related features specified in Android, saving power

and enabling faster reaction time. In particular, the LSM6DSV16X has been designed to implement hardware

features such as significant motion detection, stationary/motion detection, tilt, pedometer functions, timestamping

and to support the data acquisition of external sensors.

The LSM6DSV16X offers hardware flexibility to connect the pins with different mode connections to external

sensors to expand functionalities such as adding a sensor hub, auxiliary SPI, and so forth.

The LSM6DSV16X offers advanced design flexibility for OIS and EIS applications. Both channels have a

dedicated processing path with independent filtering and enhanced EIS channel gyroscope data are read over the

primary interfaces I²C/ MIPI I3C

v1.1 / SPI.

Channel 1 has been designed for user interface data processing for motion tracking. Data are available on the

primary output of I²C / SPI / I3C

for the accelerometer and gyroscope with independent ODR and FS.

Channel 2 has been designed for OIS applications. Data are available on the aux SPI at 7.68 kHz with

accelerometer/gyroscope processing with independent FS at ±2 g - ±16 g (accelerometer) / ±125 dps - ±2000

dps (gyroscope). The accelerometer is also available as standalone with dedicated filtering.

Channel 3 has been design for enhanced EIS. Data are available in freerun mode in the output registers or in

FIFO with dedicated tag and timestamp.

Up to 4.5 KB of FIFO with compression and dynamic allocation of significant data (that is, external sensors,

timestamp, and so forth) allows overall power saving of the system.

The LSM6DSV16X embeds a sensor fusion low-power (SFLP) algorithm able to provide a 6-axis (accelerometer

+ gyroscope) game rotation vector represented as a quaternion. The X, Y, Z quaternion components are stored in

FIFO.

Like the entire portfolio of MEMS sensor modules, the LSM6DSV16X leverages the robust and mature in-house

manufacturing processes already used for the production of micromachined accelerometers and gyroscopes. The

various sensing elements are manufactured using specialized micromachining processes, while the IC interfaces

are developed using CMOS technology that allows the design of a dedicated circuit, which is trimmed to better

match the characteristics of the sensing element.

The LSM6DSV16X embeds an analog hub, which is able to connect an external analog input and convert it to a

digital signal for processing as well as advanced dedicated features like a finite state machine and data filtering

for OIS, EIS, and motion processing.

The LSM6DSV16X embeds Qvar functionality, which is an electrostatic sensor able to measure the variation of

the quasi-electrostatic potential. The Qvar sensing channel can be used for user interface applications like tap,

double tap, triple tap, long press, and L/R – R/L swipe.

The LSM6DSV16X is available in a small plastic land grid array (LGA) package of 2.5 x 3.0 x 0.83 mm to address

ultracompact solutions.

LSM6DSV16X

Overview

DS13510 - Rev 4

page 3/198

2 Embedded low-power features

The LSM6DSV16X has been designed to be fully compliant with Android, featuring the following on-chip

functions:

• 4.5 KB FIFO data buffering, data can be compressed two or three times

– 100% efficiency with flexible configurations and partitioning

– Possibility to store timestamp

• Event-detection interrupts (fully configurable)

– Free-fall

– Wake-up

– 6D orientation

– Click and double-click sensing

– Activity/inactivity recognition

– Stationary/motion detection

• Specific IP blocks (called "embedded functions") with negligible power consumption and high performance

– Pedometer functions: step detector and step counters

– Tilt

– Significant motion detection

– Finite state machine (FSM)

– Machine learning core (MLC) with exportable features and filters for AI applications

– Adaptive self-configuration (ASC)

– Embedded sensor fusion low-power (SFLP) algorithm

• Sensor hub

– Up to six total sensors: two internal (accelerometer and gyroscope) and four external sensors

• Analog hub for processing external analog input data

• Qvar: electric charge variation detection

2.1

Pedometer functions: step detector and step counters

The LSM6DSV16X embeds an advanced pedometer with an algorithm running in an ultralow-power domain in

order to ensure extensive battery life in battery-constrained applications.

Leveraging enhanced configurability, the advanced embedded pedometer is suitable for a large range of

applications from mobile to wearable devices.

The algorithm processes and analyzes the accelerometer waveform in order to count the user's steps during

walking and running activities.

The pedometer works at 30 Hz and it is not affected by the selected device power mode (ultralow-power, low-

power, high-performance), thus guaranteeing an ultralow-power experience and extreme flexibility in conjunction

with other device functionalities.

The pedometer output can be batched in the device's FIFO buffer, in order to decrease overall system current

consumption.

ST freely provides the support and the tools for easily configuring the device and tuning the algorithm

configuration for a best-in-class user experience.

LSM6DSV16X

Embedded low-power features

DS13510 - Rev 4

page 4/198

2.2 Pedometer algorithm

The pedometer algorithm is composed of a cascade of four stages:

1. Computation of the acceleration magnitude signal in order to detect the signal independently from device

orientation;

2. FIR filter to extract relevant frequency components and to smooth the signal by cutting off high frequencies;

3. Peak detector to find the maximum and minimum of the waveform and compute the peak-to-peak value;

4. Step count: if the peak-to-peak value is greater than the settled threshold, a step is counted.

Figure 1. Four-stage pedometer algorithm

The LSM6DSV16X embeds a dynamic internal threshold for step detection that is updated after each peak-to-

peak evaluation: the internal threshold is increased with a configurable speed if a step is detected or decreased

with a configurable speed if a step is not detected.

This approach ensures high accuracy when the user starts to walk and a false peak rejection when the user is

walking or running.

An internal configurable debounce algorithm can be also set to filter false walks: indeed, an accelerometer pattern

is recognized as a walk or run only if a minimum number of steps are counted.

The LSM6DSV16X has been designed to reject a false-positive signal inside the algorithm core.

On top of the mechanisms detailed above, the LSM6DSV16X allows enabling and configuring a dedicated

false-positive rejection block to further boost pedometer accuracy.

2.3

Tilt detection

The tilt function helps to detect activity change and has been implemented in hardware using only the

accelerometer to achieve targets of both ultralow power consumption and robustness during the short duration of

dynamic accelerations.

The tilt function is based on a trigger of an event each time the device's tilt changes and can be used with

different scenarios, for example:

• Triggers when a phone is in a front pants pocket and the user goes from sitting to standing or standing to

sitting;

• Does not trigger when a phone is in a front pants pocket and the user is walking, running, or going upstairs.

2.4

Significant motion detection

The significant motion detection (SMD) function generates an interrupt when a ‘significant motion’, that could be

due to a change in user location, is detected. In the LSM6DSV16X device this function has been implemented in

hardware using only the accelerometer.

SMD functionality can be used in location-based applications in order to receive a notification indicating when the

user is changing location.

LSM6DSV16X

Pedometer algorithm

DS13510 - Rev 4

page 5/198

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zhcaeg

2024-06-27

资源内容详细，总结地很全面，与描述的内容一致，对我启发很大，学习了。
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2024-09-21

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2024-09-05

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2024-08-09

感谢资源主分享的资源解决了我当下的问题，非常有用的资源。
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2024-09-30

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