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Self-Localization

Self-Localization. Yu-Chee Tseng NCTU. External Inputs. Components of Localization. 2D/3D maps: building  frame structure  floor plan internal sensors (g-sensor, gyro) external sensors (radio, GPS, M2M) localization database. Localization Algorithms. sensor data processing

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Self-Localization

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  1. Self-Localization Yu-Chee Tseng NCTU

  2. External Inputs Components of Localization • 2D/3D maps: building frame structure  floor plan • internal sensors (g-sensor, gyro) • external sensors (radio, GPS, M2M) • localization database • Localization Algorithms • sensor data processing • data fusion (e.g., filter) • self-learning (crowdsourcing, calibrating database) • Semantics & Services • navigation • adding “semantics” to locations (office, lounge) • anticipatory reasoning and services

  3. Definition: “Self-Localization” • Infrastructure-Free: • Ex: Only utilize existing infrastructures, such as WiFi, M2M, NFC, landmarks. • Self-Content: • Ex: User devices are smart phones with those common IMU sensors. No external sensors needed. • Self-Adaptive: • Ex: Automatic transition between indoor and outdoor localization. • Ex: … and several others (algorithms, database and landmarks)

  4. Outline

  5. Outline

  6. Perceivable Landmarks in a 2.5D Space Going upstairs & downstairs Going elevators

  7. 2.5D Space Model • floor plan  extended graph • vertex = rectangle • edge = passage (with descriptors)

  8. A Particle Filter for Localization • inputs: space model, IMR, RSS pattern, radio map

  9. Outline

  10. Zero Velocity Update (1) Stance Stance

  11. Zero Velocity Update (2) • Drawback • The sensor must be mounted on the foot, which leads to large positioning error due to the excessive vibration of the foot Bad Direction Estimation

  12. Walking/Running Velocity Update WUPT Walking Running RUPT

  13. Outline

  14. Localization of Vehicles • 車用衛星導航系統的普及率越來越高，使用者對位置的精準度要求相對提高。 • 現今的車輛定位系統多以車載安全為出發，雖開發了各種不同的方法及應用，但因現今技術的限制，無法準確定位車輛位置，限制了許多應用的可行性。 • 目前定位系統無法達到車道等級，然而可達到此準確度之技術卻需要單價昂貴的設備裝置。 • 希望藉由簡單直覺的方式，提供車上系統獲得車輛的準確位置，進而輔助許多應用。 車道等級的 導航資訊 橋上/橋下 分不清楚? 事故車輛主動通知後方

  15. Real Test (實測情形) Blue cat at lane #1 (to make a right turn)

  16. Outline

  17. Lane Tracking What’s next after identifying lane number?

  18. Outline

  19. Mobile AR • Mobile Augmented Reality Applications Road Sign Recognition Signboard Advertisement ParkingBan Sign Translation National Chiao Tung University

  20. AR vs. MAR • Augmented Reality • 5 Steps • Mobile Augmented Reality • Augmented Realityruns on mobile devices 5. Augmented Information Display 2. Feature Extraction (Feature File) 3. Feature Matching 4. Augmented Information Retrieval 1. Image Capture National Chiao Tung University

  21. Possible Models • Three possibilities: • Single Machine • Client-Server • Semi-Client-Server • Can’t support large-scale system • Waste local storage Images Augmented Information Features Augmented Information • High Latency • Waste communication bandwidth, power consumption National Chiao Tung University

  22. Outline

  23. VLC • VLC = VisibleLightCommunication • broadcastingservices: • LEDs to transmit information • Photodiodes to receive information Modulated light 010101010101 1010101010 01010101 101010 01001110101001001

  24. VLC for Localization LBS service Lighting range D1 D2 I am under D4 I am under D3 and D4 Control Host D3 D4 I am under D3 Lighting Device User Location information of D3 Location information of D4

  25. Challenges • Interference problem Collision Modulated light 010101010101 1010101010 01010101 101010 01001110101001001 0101?10???01 101?101??0 01?10?0? 101?1?

  26. Prototyping Jennic module User Devices • Implementation scenario Actuators White LEDs User interface Si photodiode

  27. Thank You!

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