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Marginal Particle and Multirobot Slam: SLAM=‘SIMULTANEOUS LOCALIZATION AND MAPPING’ By Marc Sobel (Includes references to Brian Clipp Comp 790-072 Robotics)

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  • Slide 1
  • Marginal Particle and Multirobot Slam: SLAM=SIMULTANEOUS LOCALIZATION AND MAPPING By Marc Sobel (Includes references to Brian Clipp Comp 790-072 Robotics)
  • Slide 2
  • The SLAM Problem Given Given Robot controls Robot controls Nearby measurements Nearby measurements Estimate Estimate Robot state (position, orientation) Robot state (position, orientation) Map of world features Map of world features
  • Slide 3
  • SLAM Applications Images Probabilistic Robotics Indoors Space Undersea Underground
  • Slide 4
  • Outline Sensors Sensors SLAM SLAM Full vs. Online SLAM Full vs. Online SLAM Marginal Slam Marginal Slam Multirobot marginal slam Multirobot marginal slam Example Algorithms Example Algorithms Extended Kalman Filter (EKF) SLAM Extended Kalman Filter (EKF) SLAM FastSLAM (particle filter) FastSLAM (particle filter)
  • Slide 5
  • Types of Sensors Odometry Odometry Laser Ranging and Detection (LIDAR) Laser Ranging and Detection (LIDAR) Acoustic (sonar, ultrasonic) Acoustic (sonar, ultrasonic) Radar Radar Vision (monocular, stereo etc.) Vision (monocular, stereo etc.) GPS GPS Gyroscopes, Accelerometers (Inertial Navigation) Gyroscopes, Accelerometers (Inertial Navigation) Etc. Etc.
  • Slide 6
  • Sensor Characteristics Noise Noise Dimensionality of Output Dimensionality of Output LIDAR- 3D point LIDAR- 3D point Vision- Bearing only (2D ray in space) Vision- Bearing only (2D ray in space) Range Range Frame of Reference Frame of Reference Most in robot frame (Vision, LIDAR, etc.) Most in robot frame (Vision, LIDAR, etc.) GPS earth centered coordinate frame GPS earth centered coordinate frame Accelerometers/Gyros in inertial coordinate frame Accelerometers/Gyros in inertial coordinate frame
  • Slide 7
  • A Probabilistic Approach Notation: Notation:
  • Slide 8
  • Full vs. Online classical SLAM Full SLAM calculates the robot pose over all time up to time t given the signal and odometry: Full SLAM calculates the robot pose over all time up to time t given the signal and odometry: Online SLAM calculates the robot pose for the current time t Online SLAM calculates the robot pose for the current time t
  • Slide 9
  • Full vs. Online SLAM Full SLAMOnline SLAM
  • Slide 10
  • Classical Fast and EKF Slam Robot Environment: Robot Environment: (1) N distances: m t ={d(x t,L 1 ),.,d(x t,L N ) }; (1) N distances: m t ={d(x t,L 1 ),.,d(x t,L N ) }; m measures distances from landmarks at m measures distances from landmarks at time t. time t. (2) Robot pose at time t: x t. (2) Robot pose at time t: x t. (3) (Scan) Measurements at time t: z t (3) (Scan) Measurements at time t: z t Goal: Determine the poses x 1:T Goal: Determine the poses x 1:T given scans z 1:t,odometry u 1:T, and map given scans z 1:t,odometry u 1:T, and map measurements m. measurements m.