Probabilistic Robotics SLAM 1 The SLAM Problem A
Probabilistic Robotics SLAM 1
The SLAM Problem A robot is exploring an unknown, static environment. Given: • The robot’s controls • Observations of nearby features Estimate: • Map of features • Path of the robot 2
Structure of the Landmarkbased SLAM-Problem 3
SLAM Applications Indoors Undersea Space Underground 4
Representations • Grid maps or scans [Lu & Milios, 97; Gutmann, 98: Thrun 98; Burgard, 99; Konolige & Gutmann, 00; Thrun, 00; Arras, 99; Haehnel, 01; …] • Landmark-based [Leonard et al. , 98; Castelanos et al. , 99: Dissanayake et al. , 2001; Montemerlo et al. , 2002; … 5
Why is SLAM a hard problem? SLAM: robot path and map are both unknown Robot path error correlates errors in the map 6
Why is SLAM a hard problem? Robot pose uncertainty • In the real world, the mapping between • • observations and landmarks is unknown Picking wrong data associations can have catastrophic consequences Pose error correlates data associations 7
SLAM: Simultaneous Localization and Mapping • Full SLAM: Estimates entire path and map! • Online SLAM: Integrations typically done at a time Estimates most recent pose and map! 8
Graphical Model of Online SLAM: 9
Graphical Model of Full SLAM: 10
Scan Matching Maximize the likelihood of the i-th pose and map relative to the (i-1)-th pose and map. current measurement robot motion map constructed so far Calculate the map according to “mapping with known poses” based on the poses and observations. 11
Kalman Filter Algorithm 1. Algorithm Kalman_filter( mt-1, St-1, ut, zt): 2. 3. 4. Prediction: 5. 6. 7. 8. Correction: 9. Return mt, St 13
Kalman Filter Algorithm Known correspondences 14
Kalman Filter Algorithm Unknown correspondences 15
Kalman Filter Algorithm Unknown Correspondences (cont’d) 16
(E)KF-SLAM • Map with N landmarks: (3+2 N)-dimensional Gaussian • Can handle hundreds of dimensions 17
Classical Solution – The EKF • Approximate the SLAM posterior with a high • dimensional Gaussian [Smith & Cheesman, 1986] … Single hypothesis data association 18
EKF-SLAM Map Correlation matrix 19
EKF-SLAM Map Correlation matrix 20
EKF-SLAM Map Correlation matrix 21
Properties of KF-SLAM (Linear Case) [Dissanayake et al. , 2001] Theorem: In the limit the landmark estimates become fully correlated 22
Victoria Park Data Set [courtesy by E. Nebot] 23
Victoria Park Data Set Vehicle [courtesy by E. Nebot] 24
Data Acquisition [courtesy by E. Nebot] 25
SLAM [courtesy by E. Nebot] 26
Map and Trajectory Landmarks Covariance [courtesy by E. Nebot] 27
Landmark Covariance [courtesy by E. Nebot] 28
Estimated Trajectory [courtesy by E. Nebot] 29
EKF SLAM Application [courtesy by John Leonard] 30
EKF SLAM Application odometry estimated trajectory [courtesy by John Leonard] 31
EKF-SLAM Summary • Quadratic in the number of landmarks: O(n 2) • Convergence results for the linear case. • Can diverge if nonlinearities are large! • Has been applied successfully in large -scale environments. • Approximations reduce the computational complexity. 32
- Slides: 31