Robust sensor characterization via max-mixture models: GPS sensors

Large position errors plague GNSS-based sensors (e. g. , GPS) due to poor satellite configuration and multipath effects, resulting in frequent outliers. Due to quadratic cost functions when optimizing SLAM via nonlinear least square methods, a single such outlier can cause severe map distortions. Following in the footsteps of recent improvements in the robustness of SLAM optimization process, this work presents a framework for improving sensor noise characterizations by combining a machine learning approach with max-mixture error models. By using max-mixtures, the sensor's noise distribution can be modeled to a desired accuracy, with robustness to outliers. We apply the framework to the task of accurately modeling the uncertainties of consumer-grade GPS sensors. Our method estimates the observation covariances using only weighted feature vectors and a single max operator, learning parameters off-line for efficient on-line calculation.

Authors

• Ryan D. Morton
• Edwin Olson

Keywords

• Global Positioning System
• Robot sensing systems
• Uncertainty
• Noise
• Vectors
• Computational modeling
• Satellites
• Sensor Characteristics
• Global Positioning System Sensors
• Least-squares
• Error Model
• Nonlinear Least Squares
• Quadratic Cost
• Robust Improvement
• Single Outlier
• Nonlinear Least Squares Method
• Training Data
• Mixture Model
• Uncertainty Estimation
• Weight Vector
• Nonlinear Programming
• Unmanned Aerial Vehicles
• Noise Model
• Global Navigation Satellite System
• Position Estimation
• Covariance Estimation
• Dilution Of Precision
• Global Positioning System Data
• Inliers
• Gaussian Error
• Training Error
• Ground Robots
• Least-squares Optimization
• True Location
• Data Log-likelihood
• Linear Combination Of Features