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Published byKeon Hay Modified over 9 years ago
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ROBOT LOCALISATION & MAPPING: MAPPING & LIDAR By James Mead
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Content Project Overview The Team Simultaneous Localisation & Mapping (SLAM) Hardware Mapping Conclusion Questions
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Project Overview Environmental positioning, humans have it easy! The use of sensors: GPS Wheel encoder Accelerometer Gyroscope Infrared ranging sensor Ultrasonic sensor Fuse the data in software to create useable information about the robots environment
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Project Breakdown Divided into 3 major areas. Mapping & LIDAR – myself Kinect & data analysis – Scott Penley Navigation & path planning – Ken Birbeck Work together to combine all 3 aspects into one overall project. Goal: Create a robot capable of Simultaneous Localisation and Mapping (SLAM).
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What is SLAM? 2 aspects: Robot’s current position and the position of objects in the environment. Chicken or egg (robot’s position or environment layout), which comes first? My task: overcome this through the use of sensors and mathematical calculations.
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The Hardware Lynx Robot IMU – Inertial measurement unit Used for determining pitch, roll, yaw and acceleration Xbox Kinect Fit-PC Wheel encoders LIDAR – Light Detection and Ranging (Laser Rangefinder) Returns highly accurate distance data of the environment SLAM needs highly accurate sensors no GPS or digital compass
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A suitable LIDAR Range Speed Accuracy (error tolerance) Sweep angle, Weight, Dimensions, Power consumption & laser source. Price! Hokuyo URG-04LX
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Mapping Metric mapping & Topological mapping Grid Occupancy Mapping Map divided up into cells Robot’s position needs to be known accurately(X,Y, θ ) Object location converted from Polar to Cartesian Apply filters to reduce noise & correct errors
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Conclusion Familiarise with hardware Fully understand the LIDAR Develop a method for Mapping Metric or Topological mapping Incorporate the use of filters Plot the data in software Work as a team! This project will not work without Scott or Ken Any Questions?
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