Nonparametric distribution regression applied to sensor modeling

Sensor models, which specify the distribution of sensor observations, are a widely used and integral part of robotics algorithms. Observation distributions are commonly approximated by parametric models, which are limited in their expressiveness, and may require careful design to suit an application. In this paper, we propose nonparametric distribution regression as a procedure to model sensors. It is a data-driven procedure to predict distributions that makes few assumptions. We apply the procedure to model raw distributions from real sensors, and also demonstrate its utility to a mobile robot state estimation task. We show that nonparametric distribution regression adapts to characteristics in the training data, leading to realistic predictions. The same procedure competes favorably with baseline parametric models across applications. The results also help develop intuition for different sensor modeling situations. Our procedure is useful when distributions are inherently noisy, and sufficient data is available.

Authors

• Abhijeet Tallavajhula
• Barnabás Póczos
• Alonzo Kelly

Keywords

• Robot sensing systems
• Kernel
• Histograms
• Parametric statistics
• Training data
• Noise measurement
• Sensor Model
• Nonparametric Regression
• Model Parameters
• Predictive Distribution
• Mobile Robot
• Distribution Of Observations
• Histogram
• Magnetic Field
• Density Estimation
• Stationary Distribution
• Gaussian Process
• Part Of The State
• Particle Filter
• Loss Estimation
• Range Of Sensors
• Field Sensor
• Non-parametric Procedure
• Empirical Risk
• Kernel Bandwidth
• Hold-out Data
• Robot Pose
• Nominal Range
• Ray Casting
• Nonparametric Density Estimation