Navigation algorithm development requires a sensor that delivers clean, high-bandwidth inertial data — not one that introduces its own noise floor into your research.
Inertial navigation researchers and robotics teams spend significant effort distinguishing algorithm noise from sensor noise. Conventional single IMUs introduce bias and random walk that contaminate datasets, slow development cycles, and produce results that do not transfer to production hardware.
By cross-validating across a parallel MEMS array, IMS-AI delivers a sensor data stream that lets your algorithm be the variable — not the hardware. Available with a full evaluation kit including USB-serial interface, calibration data, and SDK for Python, C, and C++.
| Architecture | Parallel MEMS array with EKF + AI fusion |
| Data quality | Significantly lower noise vs single-IMU baselines |
| SDK | Python, C, C++ — ROS2 package available |
| Interface | USB-serial (eval kit) / UART / SPI / CAN (production) |
| Output | 6-DOF with post-fusion filtering |
| Detailed specs | Available under NDA to qualified programs |