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In large environments, this optimization procedure is employed to minimize estimation errors efficiently.
Differences emerge in large environments (>16 m2) in which individual place cells represent multiple, irregularly spaced locations.
In large environments, the size of a state vector becomes large because the number of feature points is large.
In large environments, it is difficult to get global optimal of the map and the camera poses due to the local minimum problem in BA.
This work presents real time implementation algorithms of Simultaneous Localization and Map Building (SLAM) with emphasis to outdoor land vehicle applications in large environments.
While the numerical test of Eq. (3) can be used to determine the operational mode, we seek a deeper understanding of why the operational mode emerges in large environments, and under what set of parameters.
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In larger environments, the megamap operates in the combinatorial mode, effectively combining two embedded attractor states to stably represent both inputs.
However, in larger environments that receive hundreds, thousands, or tens of thousands of calls per month, a more sophisticated solution is required.
This stimulus control is consistent with that seen with rotation of landmarks in larger environments, such as the water maze or the radial arm maze (Dudchenko, Goodridge, Seiterle, & Taube, 1997; Dudchenko & Taube, 1997; Suzuki, Augerinos, & Black, 1980).
Although we tested our model in environments of different size (as discussed in a following section), the model is computationally extremely demanding to run in very large environments, large enough to investigate this sort of mixed ordering.
Moreover, we intend to make more results in a large environment with more robots in order to consolidate our approach.
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