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《认知机器人》(英文版) Vision-based SLAM

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Vision-based SLAM Mobile Robot Localization And Mapping With Uncertainty using Scale-Invariant Visual Landmarks -e,lowe, Little Vikash Mansinghka Spren Riisgaard Outline
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Vision-based sLAM Mobile robot Localization And Mapping With Uncertainty using Scale-Invariant Visual Landmarks Se, Lowe, Little Vikash Mansinghka Soren Riisgaard Outline Soren SLAM SLAM introduction SIFT SLAM Experimental Results Vikash SIFT matching

Outline • • Søren: 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 & Søren Riisgaard

Simultaneous Localization And Mapping The SLAM problem · Preconditions Enough landmarks Static landmarks ANT ☆ ☆ State Est, VS SLAM State estimation EKF HMM- Viterbi HMM- Particle filters SLAM Map and robot pose is coupled Errors are correlated

• • • – – ? • 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 ·S| FT SLAM Comparison FastsLAM SIFT SLAM Robot pose Particle Filter Least Squares EKF Landmarks Combined with pose 1 Kalman Filter per 1 Kalman Filter per O(MK/o(M log K) Applications Small scenarios Large Scenarios Vision Observation Landmarks Landmarks Robot pose K= Landmarks. M= Particles SIFT SLAM Where did I try to go? Stimate Odometry based state Least squares localization estimate Where did i go · Localization-EKF Where did I really go? Mapping Update landmark cov, add new landmarks

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(K2) 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

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