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Multiple hypothesis tracking-based data association is included to be able to deal with ambiguous scenarios.
The most restrictive multiple hypothesis test correction is the Bonferroni correction.
In the following, we omit the steps under multiple hypothesis testing for clarity.
p values are adjusted for multiple hypothesis tests using Holm's method (Holm 1979).
This is a urn type inspired statistical test performing multiple hypothesis comparison.
The multiple hypothesis test correction in this case is a Bonferroni correction [16].
The Benjamini-Hochberg procedure was used to correct for multiple hypothesis testing.
Probabilistic multiple hypothesis tracking (PMHT) is an efficient approach for dealing with it.
This work introduces probabilistic multiple hypothesis anchoring to create and maintain a semantically rich world model using probabilistic anchoring.
Ambiguous information used for recognizing places is resolved with multiple hypothesis tracking and a selection procedure inspired by Markov localization.
All p-values reported are unadjusted for multiple hypothesis testing.
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