1. Underwater Vehicle Navigation Techniques Chris Barngrover CSE 237D

2.  Most funding goes to UAVs followed by UGVs  Lots of UUV applications (e.g. Moorea)  GPS is easiest way to know location, but this fails underwater  Need to use other techniques

3.  Dead Reckoning  Inertial Navigation System (INS)  Doppler Velocity Log (DVL)  Acoustic Techniques ◦ Long Baseline (LBL) ◦ Ultra-short Baseline (USBL)  Geophysical (a priori maps)  Computer Vision

4.  Microstrain 3DM-GX1 INS  SSI Technologies Pressure Sensor  2 Remote Ocean System CE-X-18 Underwater Cameras  OpenCV Library

5.  Convert pressure sensor data to depth  Develop module that subscribes to INS, depth, and vision data  Develop a Kalman filter to create position estimation  Use vision techniques to rectify position estimation

6.  Incorporated Planner Module  Developed LPS Module  Researched pressure to depth conversion  Researched Kalman filter techniques

7.  Depth Conversion Function  Basic Kalman Filter ◦ Ground up development – Stalled ◦ OpenCV Libraray - Success

8.  SSI Technologies Pressure Sensor  Take depth measurements at DepthPSI (avg)PSI (mode)STDEV < 00.4879805310.48780.002353867 00.5044682930.50820.00547204 60.56090.55920.008404093 120.6267909090.63050.005927089 180.6807153850.68150.009534933 240.7347538460.73240.007245701 360.8441807690.84460.008845203 300.7861818180.78340.006437068 360.8315666670.83440.005860034 460.9455727270.94650.004676756 520.9782536590.9770.00565677 < 00.4882915660.48780.003332674

13.  Variables:  Average Function:  Mode Function:  Amalgamation:

14.  Created a kalman library ◦ init_kalman() ◦ close_kalman() ◦ kalman_update( time, status ) ◦ kalman_get_location( &loc )  Manages the CvKalman class from OpenCV

15.  State Equation: : state vector : transition matrix - relates state vectors : control matrix – relates control to state : control vector : noise vector (k represents current time)

16.  State Equation:

17.  Measurement Equation: : measurement vector : relates state to measurement : state vector : noise vector (k represents current time)

18.  Measurement Equation:

20.  Continue Kalman Filter library ◦ Add control elements – ◦ Use angle and rotation angle to fix accelerations ◦ Add velocity sensor for better results ◦ Consider measured covariance matrices ◦ Use vision to rectify location ◦ Incorporate acoustic pinger triangulation  Other related work ◦ Build standard course with dimensions ◦ Develop visual tool