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State Estimation and Kalman Filtering

State Estimation and Kalman Filtering. Sensors and Uncertainty. Real world sensors are noisy and suffer from missing data (e.g., occlusions, GPS blackouts) Use sensor models to estimate ground truth, unobserved variables, make forecasts. How does this relate to motion?. A robot must:

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CS b553 : A lgorithms for Optimization and Learning

CS b553 : A lgorithms for Optimization and Learning. Continuous Probability Distributions and Bayesian Networks with Continuous Variables. Agenda. Continuous probability distributions Common families: The G aussian distribution Linear Gaussian Bayesian networks.

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364 views • 26 slides

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State Estimation and Kalman Filtering

CS b553 : A lgorithms for Optimization and Learning