3D Mapping Robots Intelligent Robotics School of Computer Science Jeremy Wyatt James Walker.

21
3D Mapping Robots Intelligent Robotics School of Computer Science Jeremy Wyatt James Walker
  • date post

    20-Dec-2015
  • Category

    Documents

  • view

    217
  • download

    2

Transcript of 3D Mapping Robots Intelligent Robotics School of Computer Science Jeremy Wyatt James Walker.

Page 1: 3D Mapping Robots Intelligent Robotics School of Computer Science Jeremy Wyatt James Walker.

3D Mapping Robots

Intelligent Robotics

School of Computer Science

Jeremy Wyatt

James Walker

Page 2: 3D Mapping Robots Intelligent Robotics School of Computer Science Jeremy Wyatt James Walker.

What Are 3D Mapping Robots and Their Uses?

• Robots which produce a 3-dimensional model of their environment from the data they collect

• They can be used by people who need to know more about the interior of a building:

• Architects• Fire fighters• Human rescue workers

Page 3: 3D Mapping Robots Intelligent Robotics School of Computer Science Jeremy Wyatt James Walker.

Types of Sensing Techniques

• Stereo vision• Laser range finders• A combination of the two

Page 4: 3D Mapping Robots Intelligent Robotics School of Computer Science Jeremy Wyatt James Walker.

Stereo Vision

• Use stereo disparities to compute depth

• Inaccurate in detecting the position of walls and objects especially in cluttered environments

Page 5: 3D Mapping Robots Intelligent Robotics School of Computer Science Jeremy Wyatt James Walker.

Laser Range Finders

• Very accurate in measuring distances to walls and objects in the environment

• Has a range of 8m with a resolution of 1mm and a statistical error of +/-10mm

• Can not detect any texture in the environment so can only produce single coloured models

Page 6: 3D Mapping Robots Intelligent Robotics School of Computer Science Jeremy Wyatt James Walker.

A Combination of the Two

• Laser range finders for detecting the distance of walls and objects

• An omni-cam for producing texture maps for a realistic visualisation of the environment

Page 7: 3D Mapping Robots Intelligent Robotics School of Computer Science Jeremy Wyatt James Walker.

The GATech Robot

• Equipped with a laser range finder positioned vertically to scan perpendicular to the movement of the robot

Page 8: 3D Mapping Robots Intelligent Robotics School of Computer Science Jeremy Wyatt James Walker.

How the Robot Builds the 3D Models

• Collects raw data from the environment using the laser range finder

• Converts the raw data into Cartesian co-ordinates• Converts the Cartesian co-ordinates into a mesh

for the 3D model

Page 9: 3D Mapping Robots Intelligent Robotics School of Computer Science Jeremy Wyatt James Walker.

How the Robot Collects the Raw Data

• Laser moves through 180˚ in 0.5˚ steps from one side of the robot over the top to the other recording the distance

• Approximately 38 scans are completed every second

• Robot moves forward at 0.25m/s• Therefore approximately one scan every 5cm

Page 10: 3D Mapping Robots Intelligent Robotics School of Computer Science Jeremy Wyatt James Walker.

Transforming the Raw Data Into Co-ordinates

• Raw data is in the form of cylindrical co-ordinates• Transformed using the pose of the robot, the angle

of the scan and the height of the centre of the laser scanner

Page 11: 3D Mapping Robots Intelligent Robotics School of Computer Science Jeremy Wyatt James Walker.

Collecting the Co-ordinates to Form Triangles

• Choose two scan points p1 and p2 from the same scan, taken at angles α and α + 0.5˚

• Choose the two corresponding points q1 and q2 from the next scan

• Form two triangles p1p2q1 and q1p2q2

• For each triangle calculate its normal vector

Page 12: 3D Mapping Robots Intelligent Robotics School of Computer Science Jeremy Wyatt James Walker.

GATech Model

Page 13: 3D Mapping Robots Intelligent Robotics School of Computer Science Jeremy Wyatt James Walker.

GATech Model

Page 14: 3D Mapping Robots Intelligent Robotics School of Computer Science Jeremy Wyatt James Walker.

GATech Model

Page 15: 3D Mapping Robots Intelligent Robotics School of Computer Science Jeremy Wyatt James Walker.

Disadvantages of This Approach

• The corridor appears to be slightly curved due to the way the robot moves

• Obstacles below a height of 0.52m can not be detected by the robot

• No filtering techniques were used so the model is very noisy but retains a high level of complexity because of this

Page 16: 3D Mapping Robots Intelligent Robotics School of Computer Science Jeremy Wyatt James Walker.

Further Examples: Thrun et al

• Uses two laser range finders and an omni-cam

• Uses a technique called expectation maximisation

• Processes the data to reduce the noise

Page 17: 3D Mapping Robots Intelligent Robotics School of Computer Science Jeremy Wyatt James Walker.

Expectation Maximisation

• Estimates the number of surfaces and their location

• Adds and removes surfaces until it converges on the best fit model for the data

Page 18: 3D Mapping Robots Intelligent Robotics School of Computer Science Jeremy Wyatt James Walker.

Thrun et al

Page 19: 3D Mapping Robots Intelligent Robotics School of Computer Science Jeremy Wyatt James Walker.

Thrun et al

Page 20: 3D Mapping Robots Intelligent Robotics School of Computer Science Jeremy Wyatt James Walker.

Summary

• Brief overview of what 3D mapping is and some uses for 3D mapping

• Different types of sensors used• How to collect data and convert it into a 3D model• Some more advanced methods for 3D mapping

and processing of the data

Page 21: 3D Mapping Robots Intelligent Robotics School of Computer Science Jeremy Wyatt James Walker.

References

• www.cc.gatech.edu/ai/robot-lab/research/3d/• www-2.cs.cmu.edu/~thrun/3d/