Outline ? ? S?ren: SLAM – SLAM introduction –SIFT SLAM – Experimental Results Vikash: SIFT matching Vision-based SLAM Mobile Robot Localization And Mapping With Uncertainty using Scale-Invariant Visual Landmarks - Se, Lowe, Little Vikash Mansinghka & S?ren Riisgaard ? ? ? – – ? ? Simultaneous Localization And Mapping The SLAM problem Preconditions Enough landmarks Static landmarks State Est. vs SLAM State Estimation – EKF – HMM – Viterbi – HMM – Particle filters ?SLAM – Map and robot pose is coupled – Errors are correlated 3 SLAM Algorithms ? EKF based SLAM ? FastSLAM ?SIFT SLAM ? Comparison EKF FastSLAM SIFT SLAM Robot Pose EKF Particle Filter Least Squares EKF Landmarks Combined with pose 1 Kalman Filter per Landmark/sample 1 Kalman Filter per Landmark Performance O(K 2 ) O(M K) / O(M log K) O(K) ? Applications Small scenarios Large Scenarios Vision Observation Landmarks Landmarks Robot pose K = Landmarks, M = Particles SIFT SLAM ? – ? – ? – ? – l Odometry based state estimate Where did I try to go? Least Squares localization estimate Where did I go? Localization – EKF Where did I really go? Mapping Update andmark cov, add new landmarks