Sensor Noise Covariance Tuning for Balanced Multi-Sensor Fusion

The patent applies parameter changes by systematically adjusting the covariance parameters of noise models for different sensor types. The method involves determining initial covariances from sensor data characteristics, then iteratively tuning these parameters to achieve unit variance across all sensor modalities. This transforms the raw sensor data processing into a calibrated system where each sensor's noise characteristics are properly normalized, resolving the contradiction between maintaining measurement precision and managing processing complexity.

The patent implements feedback through an iterative tuning process where the system continuously evaluates the covariance output of noise models and adjusts parameters based on residual analysis. The method computes residuals between predicted and actual sensor measurements, then uses this feedback to refine covariance estimates until convergence criteria are met. This closed-loop approach ensures that noise model parameters are optimized for accuracy while providing a systematic framework that manages complexity through automated iteration.

The patent applies preliminary action by pre-computing and storing noise model parameters and covariance characteristics during system initialization or offline calibration phases. The method establishes baseline covariance matrices and tuning parameters before actual autonomous operation begins, allowing the system to benefit from pre-optimized noise models during real-time pose graph construction and trajectory planning. This reduces the computational burden during time-critical operations while maintaining the reliability benefits of tuned noise models.

The patent implements partial action by applying noise model tuning selectively to critical sensor modalities and operations rather than uniformly to all sensors and all processing stages. The method identifies which sensor types and which phases of operation (e.g., pose graph optimization versus real-time navigation) require full covariance tuning, and applies appropriate levels of tuning effort accordingly. This selective approach achieves sufficient reliability improvement without the full computational cost of exhaustive tuning across all system components.

The patent applies equipotentiality by normalizing the covariance output of different sensor types to a common reference level (unit variance). The method transforms covariances from different sensor modalities—each originally expressed in their native units—into a standardized scale where all sensors operate from an equivalent statistical potential. This normalization enables fair comparison and proper combination of noise models across sensor types while preserving the essential characteristics of each modality, effectively creating an equipotential framework for multi-sensor integration.

The patent implements local quality by applying sensor-specific transformation parameters and scaling factors that are tailored to each sensor type's characteristics. Rather than using a uniform transformation for all sensors, the method determines individual covariance adjustment parameters based on each sensor's noise profile, measurement units, and operational characteristics. This localized approach maintains the unique qualities of each sensor modality while achieving overall consistency, allowing independent sensor processing to contribute to a unified, balanced noise model framework.