I. INTRODUCTION  
A. Taxonomy  
B. Comparison with Other Surveys  
C. Survey Organization  

II. CLASSICAL INERTIAL NAVIGATION MECHANISMS  
A. Inertial Measurement Model  
B. Strapdown Inertial Navigation System  
C. Domain Specific Knowledge  
D. Integrating IMU with Other Sensors  
E. Discussion  

III. LEARNING TO CALIBRATE INERTIAL SENSOR  

IV. LEARNING TO CORRECT IMU INTEGRATION  

V. LEARNING BASED IMU INTEGRATED POSITIONING  

VI. LEARNING TO CORRECT PEDESTRIAN INERTIAL POSITIONING  

VII. LEARNING TO CORRECT INERTIAL POSITIONING ON VEHICLES, UAV AND ROBOTIC PLATFORMS  

VIII. LEARNING SENSOR FUSION FOR IMU INTEGRATED POSITIONING APPLICATIONS  

IX. LEARNING BASED HUMAN MOTION ANALYSIS AND ACTIVITY RECOGNITION  

X. DATASETS AND EVALUATION METRICS  

XI. CONCLUSIONS AND DISCUSSIONS  
A. Benefits  
B. Challenges and Opportunities