Inertial Measurement Unit (IMU)

An Inertial Measurement Unit (IMU) is a key sensor in autonomous driving and high-precision vehicle positioning systems. By measuring a vehicle's acceleration and angular velocity, it continuously tracks its attitude and position—delivering essential, uninterrupted short-term positioning even in GPS "blind spots" like tunnels and basement car parks.

⚙️ Working Principle: Sensing "Self-Motion"

Operating independently of external signals, an IMU functions purely by detecting its own physical movements. A standard IMU integrates two core sensors:

Three-axis Accelerometer: Measures linear acceleration across three axes: longitudinal (front/back), lateral (left/right), and vertical (up/down). Integrating this acceleration over time allows changes in speed and position to be continuously calculated.
Three-axis Gyroscope: Measures rotational angular velocity across roll, pitch, and yaw. Integrating these rates continuously tracks the vehicle's attitude, determining whether it is turning, driving up an incline, or leaning into a corner.

Working in tandem, these sensors deliver a complete, real-time picture of the vehicle's motion across Six Degrees of Freedom (6DoF) — covering three translational axes and three rotational axes.

🤝 Core Value: The "Perfect Partner" to GNSS

The true strength of an IMU lies in how seamlessly it complements Global Navigation Satellite Systems (GNSS, including GPS and BeiDou).

Parameters	GNSS (Global Navigation Satellite System)	IMU (Inertial Measurement Unit)
Pros	Provides global, absolute positioning data with zero cumulative error over time.	High refresh rates (typically >100Hz) delivering continuous, smooth real-time data unaffected by external conditions.
Cons	Prone to signal dropouts (under flyovers, inside tunnels), lower update frequencies, plus noticeable latency and noise.	Subject to cumulative drift errors; standalone dead reckoning gradually drifts over time and cannot operate independently for long periods.

In advanced driver-assistance and autonomous systems, both technologies are sensor-fused using algorithms like Kalman filtering. In clear, open areas, GNSS provides absolute positioning to recalibrate and correct the IMU's accumulated drift. When entering satellite blind spots such as tunnels, the IMU steps in with high-rate, continuous dead reckoning to take over the navigation reins—maintaining precise pose estimation and buying critical reaction time for vehicle control systems. Studies have also proven that IMU sensor fusion significantly enhances the accuracy of cameras and LiDAR sensors, particularly in complex driving environments with compromised GNSS reception.