Loop-closure candidates selection by exploiting structure in vehicle trajectory

One of the most important problems in robot localisation is the detection of previously visited places (loops). When a robot closes a loop, the association between observed features and present ones can be used to update its position. The computational cost involved in the association process makes exhaustive loop search intractable. Most of the current techniques use observations of the environment as their main features to produce loop hypotheses. In this paper, we investigate the feasibility of producing loop candidates from features of the robot trajectory. We propose a new method for selecting loop-closure candidates based on an alignment likelihood function, which measures similarity between trajectory sequences. The algorithm is validated with data gathered in the city with our experimental platform. Positive results show that the trajectory has, indeed, features that can be extracted and applied to robot localisation. The resulting loop hypotheses may be regarded, for example, as a initialisation step to aid current methods.