1 Introduction
IMU (Inertial Measurement Units) sensors are the basic parts of navigation, robotics, and consumer electronics. The question of the best solution between “raw” IMU chips (resulting data are unprocessed, non-smart sensor data) and “intelligent” IMU modules (embedded in the sensor processing algorithm with dynamic sensor fusion processes and resulting processed position 6 DOF data) involves critical trade-offs in performance, finished cost, the development effort incurred, and long-term flexibility. This report provides an analysis of these dimensions based on 2025 market data, technical specifications and the implementation studies as they address the fundamental question: When should developers self-develop algorithms versus leveraging vendor-integrated solutions?
2 Technical Architecture and definitions
2.1 “Raw” IMU Chips
- Core Function: Linear acceleration (accelerometers) and angular velocity (gyroscopes). Outputs include:
- Acceleration (rad/s or °/sg)
- Angular velocity (m/s² or g)
- Data Characteristics: Not compensated or corrected for bias drift, temperature effects and sensor misalignment. demand a certain degree of external processing for purposes of pose estimation (e.g. Kalman filtering, sensor fusion)
- Example Products (2025):
- Bosch BMI323 (16-bit gyro/accel)
- STMicroelectronics ISM330BX (6 axis ready for Artificial Intelligence methods)
2.2 “Intelligent” IMU Modules
- Core Function: Transistor: Integrate the sensor fusion algorithm (e.g like extended Kalman filter) to give out the 6 Degree Of Freedom (orientation+Linear position) pose May contain magnetometers, barometers or GPS/Navigation Satellite System (GNSS) as drift correction.
- Key Features:
- On-chip edge AI Adaptive calibration (e.g. ST’s LSM6DSV16X with ML core)
- Low-latency pose output (<20 ms)
- Example Products (2025):
- SBG Systems Ellipse-N (industrial-grade, drift-corrected)
- VectorNav VN-100 (AHRS/INS module)
3.1 Drift and Stability
- Raw IMU Chips:
- Drift Accumulation: Cumulative positioning error proportional to time, uncorrected bias. For example, MEMS IMUs have experience drift in position of more than 10 meters after 60 seconds without correction.
- Key Metrics: Bias instability e.g. 13ug for ADIS16547 accelerometer, Angular Random Walk (ARW) e.g. 0.07°/√hr for ADIS16547.
- Intelligent Modules:
- Drift Mitigation: Sensor fusion (e.g. GNSS/INS), drift is mitigated (<0.1° in orientation and < 1% distance traveled over time).
- Accuracy: There are modules such as SBG Ellipse-N that have 0.05deg static orientation accuracy.
3.2 Latency and Power
- Raw IMU Chips:
- Latency: Depends on processor in the host Optical latency Typical optimized pipelines have a latency of 5 to 10 ms.
- Power: ISM330BX uses 0.6mA in high performance mode.
- Intelligent Modules:
- Latency: Latency predictor Model End-to-end Latency 20ms Fused pose output.
- Power: More because of processing on old boards (e.g. ST’s LSM6DSV16X: 15 uA in adaptive mode) .
4 Algorithm Implementation: Self or Vendor Solutions
4.1 Self Implemented Fusion Algorithms
- Advantages:
- Customization: The algorithms can be designed to adapt to a particular dynamics (e.g., drones and industrial robots).
- Transparency: Complete control over modeling the error and weighting the sensors.
- Challenges:
- Development Effort: Involves the expertise of Kalman filtering and error state estimation algorithm and sensor calibration. Studies give figures of 3 to 6 months for sound implementation.
- Calibration Complexity: Multi-stage calibration (factory, user, dynamic) is required with MEMS IMUs – causing an increase in integration time
4.2 Integrated Algorithms of Vendors
- Advantages:
- Rapid Deployment: Pre-calibrated and fused outputs minimize integration to “plug-and-play”.
- Edge AI Capabilities: ST’s ISM330BX Uses ML Cores for Real-time Noise Reduction .
- Limitations:
- Black-Box Nature: Poverty of knowledge of fusion logic, and using debugging.
- Fixed Performance: Modules such as VectorNav VN-100 don’t care as much about being precise, but limit themselves to high-speed applications due to their (relatively) limited robustness.
5 Cost Analysis and Landscape of the Market
5.1 Unit Costs (2025)
Product Table6 Comparison: Raw IMU Chip vs. Intelligent Module
IMU Chip vs Intelligent Module Comparison| Factor | Raw IMU Chip | Intelligent Module |
|---|
| Potentially higher but completely dependent upon in-house expertise. Allows for custom calibration, advanced sensor fusion and post-processing for maximum performance as per a specific application. | Good Out of the box but ceiling. Provides pre-calibrated and compensated data without deep expertise for consistent and reliable performance. Ultimate precision depends on processing in the module and cannot be tailored. |
| Long. Requires a lot of time for development needs of hardware integration, driver development, algorithm development, and need significant testing time, so product development cycle takes more time. | Short. Works as a plug and play solution. Reduces integration effort and testing by a large degree enabling quicker prototyping and faster time-to-market capabilities. |
| High demand. Requires a lot of resources in terms of skilled engineering talent (hardware and software), time and budget for R&D and validation. | Low demand. Optimization for struggling teams Offloads the complicated signal processing and algorithm development to the module’s supplier, relieving the internal team of some of the work. |
| Highly Flexible. Ideal for dynamic and unpredictable environments (e.g. aggressive drones, autonomous robots) as the informative core is available hence allowing to adapt the algorithms dynamically and to fit specific needs. | Optimized Common Dynamics. Good for anticipated dynamic ranges (e.g. Consumer drones, Stabilization). However, it has less flexibility for making low-level changes to an algorithm in real-time for novel and extreme conditions. |
| Complex and self-reliant. The team is in charge of maintaining all custom code, calibration procedures and hardware compatibility. A chip End-of-Life (EOL) can require a major redesign of the hardware. | Simplified but (unfortunately) vendor dependent. Maintenance is mostly carried out by the module supplier using firmware updates. However, this leads to vendor lock-in and a module EOL can result in a complete module change. |
7. Recommendation
- Choose a Raw IMU Chip if your project has special high-precision needs, has great in-house engineering talent and requires the freedom to write custom algorithms for a highly-dynamic application. This path provides the greatest level of control and potential performance with the cost of greatly expanded development time and resource investment.
- Choose an Intelligent Module When your priority is a fast time-to-market, you don’t have much engineering resources and you can accept a very good but not fully customizable level of performance. For a selection of inertial sensor products that serve both raw and intelligent use cases. This is the path with lower development risk and complexity but is a trade off of ultimate flexibility and can lead to a long term dependency on the module supplier.
