Abstract
Abstract Autonomous navigation on sidewalks and pedestrian areas is a complex problem, that requires the solution of different challenging tasks. One that is particularly hard to tackle is that of autonomous street crossing, which requires the robot to be aware of the position and speed of surrounding vehicles in order to decide whether is safe to cross. This work is dedicated to the development of an obstacle speed estimation algorithm to be applied to the context of autonomous navigation at crosswalks. In particular, a novel approach to the extended-target tracking problem is presented, which leverages a nested structure and a clustering algorithm that reduces the problem to a standard target tracking one. The effectiveness of the algorithm is demonstrated through testing on a prototype parcel-delivery robot operating in a real-world urban environment.
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