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Volcano feature locations taken from GVP's VOTW database.
Viewshed measurements are taken both from rock feature locations and to rock feature locations from the main route of travel through the area: the Fraser River.
In the update step, the predictions of feature locations are compared to the observations.
Therefore, in addition to improved estimates of the feature locations, we can identify visual features that correspond to foreign structure on the ship hull.
In another variant [82], separate boosted cascade detectors, one for each landmark, model shapes implicitly by learning the pairwise distribution of all true feature locations.
The general structure of the method bears some resemblance to a Kalman filter in the sense that it uses a prior vehicle state estimate to predict current feature locations and then compares this prediction to current observations to calculate an updated vehicle state.
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This work presents our FeLLaCaM approach for Feature Location.
In our evaluation, we compare our FeLLaCaM approach with two other approaches for Feature Location: (1) FLL (Feature Location through Latent Semantic Analysis) and (2) FLC (Feature Location through Comparisons).
Single feature location delay is several times of the multi-feature fusion and localization.
Feature Location could be useful in achieving the transition to SPLs.
In the worst case, the single feature location delay is about 150 times of the multi-feature fusion.
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