Navigation capability in complex and unknown outdoor environments is one of the major requirements for an autonomous vehicle and a robot that perform tasks such as a military mission or planetary exploration. Robust traversabilityestimationinunknownenvironmentswouldallow thevehicleortherobottodevisecontrolandplanningstrategiestomaximizetheireffectiveness.Inthisstudy,wepresent a self-supervised on-line learning architecture to estimate the traversability in complex and unknown outdoor environments. The proposed approach builds a model by clustering appearance data using the newly proposed incremental nonparametric Bayesian clustering algorithm. The clusters are thenclassifiedasbeingeithertraversableornon-traversable. Because our approach effectively groups unknown regions with similar properties, while the vehicle is in motion without human intervention, the vehicle can be deployed to new environments by automatically adapting to changing environmental conditions. We demonstrate the performance of the proposed clustering algorithm through intensive experimentsusingsyntheticandrealdataandevaluatetheviabilityof the traversability estimation using real data sets collected in outdoor environment.

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• An incremental nonparametric Bayesian clustering-based traversable region detection method