Probabilistic Robotics Robot Localization
2 Localization Given Map of the environment. Sequence of sensor measurements. Wanted Estimate of the robot’s position. Problem classes Position tracking Global localization Kidnapped robot problem (recovery) “Using sensory information to locate the robot in its environment is the most fundamental problem to providing a mobile robot with autonomous capabilities.” [Cox ’91]
3 Localization
4 Position tracking
5 Localization Global localization
6 Landmark-based Localization
7 Linearity Assumption Revisited
8 Non-linear Function
9 EKF Linearization (1)
10 EKF Linearization (2)
11 EKF Linearization (3)
12 Prediction: Correction: EKF Linearization: First Order Taylor Series Expansion
13 EKF Algorithm 1.Extended_Kalman_filter ( t-1, t-1, u t, z t ): 2. Prediction: Correction: Return t, t
14 1.EKF_localization ( t-1, t-1, u t, z t, m): Prediction: Motion noise Jacobian of g w.r.t location Predicted mean Predicted covariance Jacobian of g w.r.t control
15 1.EKF_localization ( t-1, t-1, u t, z t, m): Correction: Predicted measurement mean Pred. measurement covariance Kalman gain Updated mean Updated covariance Jacobian of h w.r.t location
16 EKF Prediction Step (known correspondences)
17 EKF Correction Step (known correspondences)
18 EKF Prediction Step (unknown correspondences)
19 EKF Correction Step (unknown correspondences)
20 EKF Prediction Step
21 EKF Observation Prediction Step
22 EKF Correction Step
23 Estimation Sequence (1)
24 Estimation Sequence (2)
25 Comparison to GroundTruth
26 EKF Summary Highly efficient: Polynomial in measurement dimensionality k and state dimensionality n: O(k n 2 ) Not optimal! Can diverge if nonlinearities are large! Works surprisingly well even when all assumptions are violated!
27 Linearization via Unscented Transform EKF UKF
28 UKF Sigma-Point Estimate (2) EKF UKF
29 UKF Sigma-Point Estimate (3) EKF UKF
30 Unscented Transform Sigma points Weights Pass sigma points through nonlinear function Recover mean and covariance
31 UKF_localization ( t-1, t-1, u t, z t, m): Prediction: Motion noise Measurement noise Augmented state mean Augmented covariance Sigma points Prediction of sigma points Predicted mean Predicted covariance
32 UKF_localization ( t-1, t-1, u t, z t, m): Correction: Measurement sigma points Predicted measurement mean Pred. measurement covariance Cross-covariance Kalman gain Updated mean Updated covariance
33 UKF Prediction Step
34 UKF Observation Prediction Step
35 UKF Correction Step
36 EKF Correction Step
37 Estimation Sequence EKF PF UKF
38 Estimation Sequence EKF UKF
39 Prediction Quality EKF UKF
40 UKF Summary Highly efficient: Same complexity as EKF, with a constant factor slower in typical practical applications Better linearization than EKF: Accurate in first two terms of Taylor expansion (EKF only first term) Derivative-free: No Jacobians needed Still not optimal!
41 [Arras et al. 98]: Laser range-finder and vision High precision (<1cm accuracy) Kalman Filter-based System [Courtesy of Kai Arras]
42 Map-based Localization
43 Monte Carlo (Particle Filter) Localization
44 Resampling Algorithm
45 Monte Carlo (Particle Filter) Localization
46 Monte Carlo (Particle Filter) Localization 1.Algorithm sample_normal_distribution( b ): 2.return 1.Algorithm sample_triangular_distribution( b ): 2.return
47 Monte Carlo (Particle Filter) Localization
48 Monte Carlo (Particle Filter) Localization
49 Monte Carlo (Particle Filter) Localization
50 Monte Carlo (Particle Filter) Localization
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66 Monte Carlo (Particle Filter) Localization
67 Monte Carlo (Particle Filter) Localization
68 Multi- hypothesis Tracking
69 Belief is represented by multiple hypotheses Each hypothesis is tracked by a Kalman filter Additional problems: Data association: Which observation corresponds to which hypothesis? Hypothesis management: When to add / delete hypotheses? Huge body of literature on target tracking, motion correspondence etc. Localization With MHT
70 MHT: Implemented System (2) Courtesy of P. Jensfelt and S. Kristensen
71 MHT: Implemented System (3) Example run Map and trajectory # hypotheses #hypotheses vs. time P(H best ) Courtesy of P. Jensfelt and S. Kristensen