Have you ever encountered situations where a drone is hovering in mid-air—yet, even without any control inputs via the joystick, it begins to gradually deviate from its original position; an autonomous vehicle has been driving for several minutes through a tunnel, yet its location marker on the navigation map drifts outside the road boundary; or a smartphone's navigation app leads you into an underground garage, with the arrow pointing beginning to rotate chaotically... Behind these phenomena lies a common culprit: the cumulative error inherent in inertial navigation systems.

Why does an IMU tend to "deviate further"?

The core component of inertial navigation is the Inertial Measurement Unit (IMU), which consists of an accelerometer and a gyroscope. Its operating principle is straightforward: the accelerometer measures acceleration; integrating this acceleration once yields velocity, and integrating that velocity once yields position; meanwhile, the gyroscope measures angular velocity, and integrating this angular velocity yields the orientation angle. The IMU does not rely on any external signals; it determines its position and attitude entirely through its own internal computations—this is both its advantage and its Achilles' heel.

The issue is that real-world sensors are far from ideal models. Even when an IMU is completely stationary, the gyroscope will output a tiny angular velocity—this is known as the gyroscope zero drift. This seemingly negligible small error, once integrated, can lead to continuous attitude drift. More critically, this attitude error can "contaminate" both the velocity and position data through coordinate transformation—wherein the position error grows according to a cubic function of time.

The accelerometer operates similarly: at rest, it should output the gravitational acceleration g, but in practice, it exhibits an additional small zero offset. This zero offset is integrated once to produce a velocity error, and then integrated again to produce a position error—where the position error grows quadratically over time.

This is the "natural enemy" of inertial navigation: errors will only accumulate and will never dissipate on their own. In unaided pure inertial navigation, the position estimate will become increasingly inaccurate.

How does LINS Technology combat cumulative errors?

While cumulative error is a "natural limitation" of inertial navigation systems, it is not insurmountable. A common approach within the industry involves fusing data from the IMU with other systems—such as satellite navigation or visual navigation—to periodically calibrate the cumulative errors of the inertial navigation system using external signals. However, the most fundamental solutions boil down to two approaches: improving the accuracy of the sensors themselves, and employing algorithms to "eliminate" these errors. As a leading domestic manufacturer specializing in the research and development of inertial sensing technologies, LINS Technology's full range of inertial navigation products serves as a prime example of how these three key strategies—hardware optimization, calibration, and algorithmic refinement—can effectively combat cumulative errors.

At the hardware level, LINS Technology reduces initial errors at both the core component and process design stages. For instance, the LINS-688 series employs a 3D stacked MEMS process, which significantly shortens internal signal paths and minimizes transmission interference; its performance—specifically, the gyro's zero bias instability (<2°/h) and the accelerometer's zero bias instability (<50 μg)—far exceeds industry averages. Furthermore, combined with dedicated electromagnetic compatibility (EMC) and noise reduction circuits, as well as high-reliability structural designs, the system effectively shields against measurement noise and performance offsets caused by external environmental factors such as electromagnetic interference and mechanical vibration, thereby substantially extending the effective operating duration of the pure inertial navigation system in standalone operation and addressing the "drift" issue at its root level.

At the calibration stage, LINS Technology employs a series of high-performance high-and low-temperature ovens and high-precision rotary stages to perform rigorous temperature compensation and calibration for each IMU, correcting zero offset caused by temperature drift, cross-coupling between sensors across different axes, and nonlinear errors. This strategy—of minimizing errors as much as possible prior to shipment—establishes a robust defense against cumulative errors.

At the algorithmic level, LINS Technology's products generally employ Extended Kalman Filter (EKF) or Multi-source Fusion Kalman Filter algorithms. Taking the MS-6222 module-level high-precision MEMS-based combined navigation system as an example, it integrates a self-developed high-precision IMU and a high-precision dual-antenna GNSS module; utilizing a multi-source Kalman filter fusion algorithm, it employs satellite positioning to perform real-time correction of the IMU's cumulative errors when GNSS signals are available, and relies on the IMU to maintain short-term high-precision positioning when GNSS is locked out. Under lockout conditions, the horizontal position accuracy can be controlled within 0.2% × D – that is, after satellite signal loss, the total positioning error of the system will not exceed 0.2% of its accumulated travel distance.

In reality, the "drift" observed in inertial navigation is always relative; what we can do is continuously push the upper limit of hardware accuracy higher, combined with rigorous calibration tests and optimization of fusion algorithms, thereby making the results from position and attitude measurement increasingly closer to the true values. Technological breakthroughs by domestic sensing enterprises such as LINS Technology have not only broken the monopoly held by foreign manufacturers in the high-end inertial sensing sector but have also enabled downstream industries—such as commercial aerospace, drones, and embodied intelligence—to access higher-precision inertial measurement capabilities at lower costs, effectively addressing critical pain points within the industry.