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Zhuoling Xiao, Hongkai Wen, Andrew Markham, Niki Trigoni
Lightweight Map Matching for Indoor Localisation using Conditional Random Fields Zhuoling Xiao, Hongkai Wen, Andrew Markham, Niki Trigoni Presented By Stephen Xia for Columbia University ELEN 9705 Spring 2018 Instructor: Prof. Xiaofan (Fred) Jiang February 22, 2018 Proceedings of the 13th International Symposium on Information Processing in Sensor Networks IPSN 2014, Berlin, Germany April 15th – 17th, 2014
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Computationally Efficient
Claims Robustness Computationally Efficient Training Required? MapCraft x no IMU Motion Sensing RSS Fingerprinting yes IMU + RSS Fusion
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Related Works
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IMU Motion Sensing Issues: Compounding Errors
Variability in walking and phone position Use of gyroscope increases power consumption
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RSS-based Fingerprinting
Issues: Intensive “training” phase
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+ IMU + RSS fusion Issues:
Requires both IMU + RSS sensors to be robust Computationally Inefficient
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Conditional Random Fields (CRF)
Maximize Entropy
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CRF vs Bayesian Methods
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Methodology
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Map Matching via CRF Define Three Features Training or Preset
Location Estimation
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CRF First Feature Raw Inertial Measurements - Heading and Displacement
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CRF Second Feature Corrected Inertial Measurements
Shen Y, Tseng K, Wu F. A Motion Tracking and Inertial Measurement Unit–Distance Sensor Fusion Module for Medical Simulation1. ASME. J. Med. Devices. 2016;10(2): doi: /
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CRF Third Feature RSS Mapping – Optional and if available
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Training Forward-Backward Algorithm
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Location Estimation Viterbi Dynamic Programming Algorithm
States are locations determined by input map/floorplan
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MapCraft
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Evaluation Robustness Efficiency Training and Generalizability
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Evaluation - Robustness
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Evaluation - Efficiency
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Evaluation – Training/Generalizability
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Evaluation – Experiments
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Questions and Discussion
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