Data Association in Stochastic Mapping Using the Joint Compatibility Test

Data Association in Stochastic Mapping Using the Joint Compatibility Test (2001)

by Jose Neira , Juan D. Tards

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Datum	Value	Source
TITLE	Data Association in Stochastic Mapping Using the Joint Compatibility Test	SVM HeaderParse 0.1
AUTHOR NAME	Jose Neira	user correction - Legacy Corrections
AUTHOR NAME	Juan D. Tards	user correction - Legacy Corrections
ABSTRACT	In this paper, we address the problem of robust data association for simultaneous vehicle localization and map building. We show that the classical gated nearest neighbor approach, which considers each matching between sensor observations and features independently, ignores the fact that measurement prediction errors are correlated. This leads to easily accepting incorrect matchings when clutter or vehicle errors increase. We propose a new measurement of the joint compatibility of a set of pairings that successfully rejects spurious matchings. We show experimentally that this restrictive criterion can be used to efficiently search for the best solution to data association. Unlike the nearest neighbor, this method provides a robust solution in complex situations, such as cluttered environments or when revisiting previously mapped regions.	user correction - Legacy Corrections
YEAR	2001	user correction - Legacy Corrections
CITATIONS	24 found	ParsCit 1.0