Fig. 3. Three navigation tracks from the ROV Jason, lowering #230. The "DVL" track shows the dead-reckoning resulting from the DVL odometry alone. The "Exact LBL" track shows the standalone LBL solution. The "EKF Estimate" track shows the combination of both the DVL and LBL information using an extended Kalman filter framework. All tracks are started at the "Origin". The tracklines were executed over 3.5 hours at an average depth of 2,265 m.
The LBL position solution complements the DVL dead-reckoning. Returning to Fig. 3 we see that the Exact LBL provides a solution with bounded uncertainty, but with a high degree of random errors or noise. We can see outliers (shown by widely spaced data points) and zones where no LBL is returns are received (eg., the Exact LBL track dissappears in the northwest corner of the figure). A particularly insidious form of error is the consistent, but off-set position solutions shown in the southwest section of the survey. This type of error can be difficult to filter autonomously.
Finally, to illustrate the possibility of leveraging the complementary nature of the two navigation tracks, we show the results of an extended Kalman filter (EKF) estimator. This track uses absolute positioning from the LBL source to constrain the unbounded uncertainty in the DVL dead-reckoning. By simultaneously using both sources of information, the EKF solution combines the strengths of both methods. This example highlights the contrasts between the unbounded uncertainty of DVL dead-reckoning, the bounded uncertainty of LBL positioning and the utility of combining these two solutions.
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