Autonomous robots require many types of information to obtain intelligent and safe behaviours. For outdoor operations, semantic mapping is essential and this paper proposes a stochastic automaton to localise the robot within the semantic map. For correct modelling and classi¯cation under uncertainty, this paper suggests quantising robotic perceptual features, according to a probabilistic description, and then optimising the quantisation. The proposed method is compared with other state-of-the-art techniques that can assess the con¯dence of their classi¯cation. Data recorded on an autonomous agricultural robot are used for veri¯cation and the new method is shown to compare very favourably with existing ones.
- Probabilistic models
- Stochastic automata