Quadrotor State Estimation and Obstacle Detection Robot Autonomy - - PowerPoint PPT Presentation

quadrotor state estimation and obstacle detection
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Quadrotor State Estimation and Obstacle Detection Robot Autonomy - - PowerPoint PPT Presentation

Quadrotor State Estimation and Obstacle Detection Robot Autonomy Project Cole, Job, Erik, Rohan I. Dynamics II. Differential Flatness III. Planning IV. Control Architecture V. State Estimation (EKF) VI. Sensors VII. SLAM (RTAB Map)


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Quadrotor State Estimation and Obstacle Detection

Robot Autonomy Project Cole, Job, Erik, Rohan

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I. Dynamics II. Differential Flatness III. Planning IV. Control Architecture V. State Estimation (EKF) VI. Sensors VII. SLAM (RTAB Map) VIII. Obstacle Detection IX. Video

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Quadrotor Dynamics

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Pick outputs:

Differential Flatness

X Y Z Phi Theta Psi Any 4 of the following 6 can serve as flat outputs: Such that:

Murray, Richard M., Muruhan Rathinam, and Willem Sluis. "Differential flatness

  • f mechanical control systems: A catalog of prototype systems."ASME

international mechanical engineering congress and exposition. 1995.

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SLIDE 5

Differential Flatness

X Y Z Phi Theta Psi Any 4 of the following 6 can serve as flat outputs: Trajectory Planning: kr = 4, kψ = 2

Mellinger, Daniel, Nathan Michael, and Vijay Kumar. "Trajectory generation and control for precise aggressive maneuvers with quadrotors." The International Journal of Robotics Research (2012): 0278364911434236.

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SLIDE 6

CONTROL ARCHITECTURE

Mahony, Robert, Vijay Kumar, and Peter Corke. "Multirotor aerial vehicles: Modeling, estimation, and control of quadrotor." IEEE Robotics & amp amp Automation Magazine 19 (2012): 20-32.

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Sensors Camera IMU Height Sensor Sony Playstation Eye source: http://amazon.com PX4FLOW KIT source: https://pixhawk.org

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Extended Kalman Filter

Position Updates from EKF

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State Estimation with Optical Flow

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State Estimation with Optical Flow

Velocity Updates from Optical Flow Camera Position Updates from EKF

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State Estimation with Optical Flow

Odometry Readings Linear Drift with Time in Simulation

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RTAB-Map

  • Graph and Node based System
  • Gathers RGB and Depth information
  • OpenNI handles point clouds
  • Uses visual words to detect loop closures
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RGB-D SLAM

Static Map Dynamic Map Building

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Obstacle Detection

RBG-D Point Cloud Data Occupancy Grid

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Visualization of Costmap and State Estimation