1. 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

2. Computationally Efficient
Claims
Robustness
Computationally Efficient
Training Required?
MapCraft x no
IMU Motion Sensing
RSS Fingerprinting
yes
IMU + RSS Fusion

3. Related Works

4. IMU Motion Sensing Issues: Compounding Errors
Variability in walking and phone position
Use of gyroscope increases power consumption

5. RSS-based Fingerprinting
Issues:
Intensive “training” phase

6. + IMU + RSS fusion Issues:
Requires both IMU + RSS sensors to be robust
Computationally Inefficient

7. Conditional Random Fields (CRF)
Maximize Entropy

8. CRF vs Bayesian Methods

9. Methodology

10. Map Matching via CRF Define Three Features Training or Preset
Location Estimation

11. CRF First Feature Raw Inertial Measurements - Heading and Displacement

12. 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: /

13. CRF Third Feature
RSS Mapping – Optional and if available

14. Training Forward-Backward Algorithm

15. Location Estimation Viterbi Dynamic Programming Algorithm
States are locations determined by input map/floorplan

16. MapCraft

17. Evaluation
Robustness
Efficiency
Training and Generalizability

18. Evaluation - Robustness

19. Evaluation - Efficiency

20. Evaluation – Training/Generalizability

21. Evaluation – Experiments

22. Questions and Discussion