MCL -> SLAM

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# MCL -> SLAM - PowerPoint PPT Presentation

MCL -> SLAM. x : pose m : map u : robot motions z : observations. The SLAM Problem. Given : Robot’s controls ( u ) Observations of features ( z ) Produce : -Map of features ( m ) -Path of the robot ( x ). Indoors. Undersea. Underground. Space. Can’t map without a pose!

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## MCL -> SLAM

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Presentation Transcript
1. MCL -> SLAM

2. x: pose m: map u: robot motions z: observations

3. The SLAM Problem • Given: • Robot’s controls (u) • Observations of features (z) • Produce: • -Map of features (m) • -Path of the robot (x)

4. Indoors Undersea Underground Space

5. Can’t map without a pose! Can’t localize without a map!

6. Given: • Robot’s controls (u) • Observations of features (z) • Map of features (m) • Produce: • -Path of the robot (x) • Given: • Robot’s controls (u) • Observations of features (z) • Produce: • -Map of features (m) • -Path of the robot (x)

7. x, y,  Landmark 1 Landmark 2 Landmark M … So, what’s a particle? Particle #1 x, y,  Landmark 1 Landmark 2 Landmark M … Particle #2 x, y,  Landmark 1 Landmark 2 Landmark M … … Particle N

8. So how do we represent a landmark? MCL Landmark’s location: known Landmark: SLAM Landmark’s location: estimated Landmark: covariance matrix

9. So how do we represent a landmark? MCL Landmark’s location: known Landmark: SLAM Landmark’s location: estimated Landmark: covariance matrix

10. How do we run this particle filter? =robot_motion obs=robot_observations foreachp in Particles p.weight=1 p.move( ) // Motion Model! foreach in obs if never seen before p.map.add_landmark( ) // Sensor Model! else p.map.update_landmark( ) // Sensor Model! p.weight=p.weight * p.update_weight( ) // Independence! resample()

11. FastSLAM – Action Update Landmark #1 Filter Particle #1 Landmark #2 Filter Particle #2 Particle #3

12. FastSLAM – Sensor Update Landmark #1 Filter Particle #1 Landmark #2 Filter Particle #2 Particle #3

13. Weight = 0.8 Weight = 0.4 Weight = 0.1 FastSLAM – Sensor Update Particle #1 Particle #2 Particle #3

14. Discussion Questions • What happens to one landmark’s estimated location if another landmark is observed wrongly? • What will happen if each landmark is seen exactly once in a trajectory? • What happens if the robot should see a landmark, and doesn’t? (negative information) • What happens if the robot misidentifies a landmark?

15. Discussion Questions • What if your robot gets re-kidnapped? • What will happen if your robot never moves? • What happens if the robot should see a landmark, and doesn’t? (negative information) • What happens if the robot misidentifies a landmark?