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Laser Based Personal Navigation System

Laser Based Personal Navigation System. Jari Saarinen and Seppo Heikkilä Helsinki University of Technology Automation Technology laboratory Finland. Project funded by the European Community under the IST programme Future and Emerging Technologies.

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Laser Based Personal Navigation System

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  1. Laser Based Personal Navigation System Jari Saarinen and Seppo Heikkilä Helsinki University of Technology Automation Technology laboratory Finland Project funded by the European Community under the IST programme Future and Emerging Technologies Jari Saarinen

  2. IntroductionPeLoTe = Building Presence through Localization for Hybrid Telematic Systems Scenario • Human and Robotic entities explore common area with the help of an operator • Both provide continuous data from environment • Case example: Fire fighting • How to incorporate human into a telematic system? Jari Saarinen

  3. Personal Navigation System - PeNa • Standalone indoor localisation system • Stride length measurement unit • Compass • Gyro • Laser odometry • Map based localisation • Laser also used for metric mapping • WLAN, UI, Power, Camera, Audio Jari Saarinen

  4. Personal Localisation and Mapping • Laser range finder is widely used in robotics for localisation, mapping and SLAM. • Main challenges: • Non static orientation • Floor and ceiling echoes • False distances • Rapid movements • No kinematic model • No control inputs Jari Saarinen

  5. Localisation Procedure • The localisation is divided into three stages: • Dead Reckoning using “inertial” sensors • Dead reckoning using Laser • Map based localisation Jari Saarinen

  6. Dead Reckoning • Absolute heading from compass fused with solid state gyros (3DM-G) • SiLMU measures continuously the step length • Two Implementations based on US and accelerometers • Used with heading information the result is comparable to the odometry Jari Saarinen

  7. Laser Dead Reckoning • Heading and translation are calculated separately • Histogram based rotation correction • 2D correlation for translation • Coarse-to-fine search Jari Saarinen

  8. Monte Carlo Localisation • Map Based Localisation • A Structural Body derived from CAD map • The map is expected to be partly correct • A particle filter is run in real time to update the position estimate Jari Saarinen

  9. Results • Dead Reckoning • 5-10% error from distance travelled Laser DR - 2-5% error from distance travelled Map Based result - Bounded error Jari Saarinen

  10. Results - II Jari Saarinen

  11. Conclusions and future work • Standalone indoor localisation system for human was presented • Using such a system the human can be a part of the telematic system • This opens new possibilities for creating cooperation between humans and robots • The system will increase the situational awareness for the group • Future work is to make the system really usable for rescue purposes (choice of sensors, size, weight, robustness) Jari Saarinen

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  16. Technical Details • Weight approx 14kg without laptops (20kg with) • 7Ah @ 24VDC batteries (laptops use own batteries) • Operation time approx. 2h • Dead reckoning accuracy from 2% - 10% from distance travelled • Map based localisation accuracy approx 0.5m with 5 deg angle accuracy (std) depending on the accuracy of the map Jari Saarinen

  17. Using Laser For Localisation • The laser scan matching provides not only information about movement but also environmental data (Map) • Laser odometry provides accurate estimate of movement • Main problems are swinging and placement in human body, especially floor and ceiling echoes Jari Saarinen

  18. Laser Based Algorithms • Scan Matching • The result from dead reckoning used as an initial estimate • Histogram based rotation correction • 2D correlation for translation • Map Based Localisation • A Structural Body derived from CAD map (or manually, or automatically made from paper drawing) • Monte Carlo Localisation algorithm Jari Saarinen

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