From the early beginning, the Simultaneous Localization And Mapping (SLAM) problem has been approached using a probabilistic background. A new solution based on the Transferable Belief Model (TBM) framework is proposed in this article. It appears that this representation of knowledge affords numerous advantages over the classic probabilistic ones and leads to particularly good performances (an average of 3.2% translation drift and 0.0040deg/m rotation drift), especially when it comes to crowded environment. By introducing the basic concepts of a Credibilist SLAM, this article aims at proving that the use of this new theoretical context opens a lot of perspectives for the SLAM community.

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Vehicle and pedestrian collisions often result in fatality to the vulnerable road users (VRU), indicating a strong need of technologies to protect such persons. Laser sensors have been extensively used for moving obstacles detection and tracking. Laser impacts are produced by reflection on these obstacles which suggests that more information is available for their classification. This paper proposes a new system to address this issue. We introduce the design of our system that is divided in three parts : definition of geometric features describing road obstacles, multiclass object classification from an adaboost trained classifier and track class assignment by integrating consecutive classification decision values. During this study, we show how specific features adapted to urban obstacles enhance the state of the art method for person detection in 2D laser data. Hence, in this paper, we evaluate usefulness of each feature and list the best ones. Moreover, we investigate the influence of laser height for each class showing that classification performance depends on the sensor position. Finally, we tested our system on some laser sequences and showed that it can estimate the class of some road obstacles around the vehicle with an accuracy of 87.4%.

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