Abstract
A vessel navigating in a critical environment such as an archipelago requires very accurate movement estimates. Intentional or unintentional jamming makes GPS unreliable as the only source of information and an additional independent supporting navigation system should be used. In this paper, we suggest estimating the vessel movements using a sequence of radar images from the preexisting body-fixed radar. Island landmarks in the radar scans are tracked between multiple scans using visual features. This provides information not only about the position of the vessel but also of its course and velocity. We present here a navigation framework that requires no additional hardware than the already existing naval radar sensor. Experiments show that visual radar features can be used to accurately estimate the vessel trajectory over an extensive data set.
Highlights
In autonomous robotics, there is a need to accurately estimate the movements of a vehicle
A simple movement sensor like a wheel encoder on a ground robot or a pit log on a vessel will under ideal circumstances provide quite accurate movement measurements
Wheel slip due to a wet surface will be interpreted incorrectly by a wheel encoder, and strong currents will not be correctly registered by the pit log why a position estimate based solely on these sensors will drift off
Summary
There is a need to accurately estimate the movements of a vehicle. Radar scan matching to estimate relative movements is studied. When these features are tracked using a filter, estimates of the vessel movements are obtained that over time give an accurate trajectory estimate. In [13], SLAM was performed in both urban and rural areas by aligning the latest radar scan with the radar map using 3D correlations to estimate the relative movements of the vehicle.
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