Multi-IMU Combination Unit for Sensor Fusion and Calibration
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Solution Overview
Problem
Existing approaches for combining inertial data measurements from multiple inertial data sensors in autonomous vehicles face challenges such as calibration issues, increased computational workload, and sensitivity to time-sync offsets, which can lead to degraded performance and single-point failure risks.
Innovation Solution
A multi-IMU combination unit (MICU) that functions upstream of inertial measurement data consumers, capable of simultaneously combining and calibrating inertial data from any number of sensors, correcting for biases and mounting errors, and providing a single inertial measurement, thereby making consumers agnostic to sensor faults.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple inertial data sensors are combined to improve reliability and reduce single-point failure risks, then system reliability is improved, but device complexity and computational workload increase
Solution Approach 1:
The patent combines multiple inertial data sensors into a unified processing architecture where sensor data is integrated through a common estimation algorithm. This merging approach maintains reliability benefits while reducing overall system complexity by consolidating processing functions rather than maintaining separate processing chains for each sensor.
Solution Approach 2:
The estimation algorithm serves multiple functions simultaneously: it processes data from any number of inertial sensors, performs calibration, compensates for mounting errors, and provides unified output to consumers. This multi-functionality reduces the need for separate specialized components, thereby reducing device complexity while maintaining reliability.
2Measurement precision
If multiple inertial data sensors are combined to improve measurement precision, then measurement precision is improved, but computational workload increases
Solution Approach 1:
The system dynamically adjusts processing parameters based on the number of active sensors and operational conditions. The estimation algorithm optimizes computation by adapting to varying input configurations, maintaining high measurement precision while minimizing unnecessary computational operations that would increase energy consumption.
3Measurement precision
If calibration is performed for each inertial data sensor individually to improve calibration accuracy, then calibration accuracy is improved, but device complexity and time consumption increase
Solution Approach 1:
The calibration process merges individual sensor calibration into a unified procedure. The estimation algorithm simultaneously calibrates multiple sensors by processing their combined data, achieving accurate calibration results while reducing the time required compared to sequential individual calibration of each sensor.
4Reliability
If inertial data from multiple sensors is fused to reduce sensitivity to time-sync offsets, then reliability is improved, but computational complexity increases
Solution Approach 1:
The estimation algorithm incorporates feedback mechanisms that monitor the quality and timing of input data from multiple sensors. This feedback allows the system to dynamically adjust processing to compensate for time-sync variations, achieving robustness without requiring complex pre-synchronization infrastructure or computationally intensive correction algorithms.
Data Source
AI summary
Disclosed are embodiments for facilitating a multi-inertial measurement unit (IMU) combination unit. In some aspects, an embodiment includes receiving, at a multi-IMU combination unit (MICU), sensor data from a plurality of inertial data sensors of a same sensor type; for each inertial data sensor, calibrating and transforming the respective sensor data using a calibration estimate for the inertial data sensor, where the calibration estimate is based on pre-integration methods that provide individual kinematic feedback that is compared to fused kinematic feedback from a main filter; combining the calibrated and transformed sensor data from the plurality of inertial data sensors into a fused output for the same sensor type; sampling the fused output to provide a single inertial data measurement for the plurality of inertial data sensors; and providing the fused kinematic feedback for the calibration estimate, the fused kinematic feedback generated from the sampling of the single fused output.