Multiple Hypothesis for Object Class Disambiguation from Multiple Observations

The current paper addresses the problem of object identification from multiple3D partial views, collected from different view angles with the objective of disambiguating between similar objects. We assume a mobile robot equipped with a depth sensor that autonomously collects observations from an object from different positions, with no previous known pattern. The challenge is to efficiently combine the set of observations into a single classification. We approach the problem with a multiple hypothesis filter that allows to combine information from a sequence of observations given the robot movement. We further innovate by off-line learning neighborhoods between possible hypothesis based on the similarity of observations. Such neighborhoods translate directly the ambiguity between objects, and allow to transfer the knowledge of one object to the other. In this paper we introduce our algorithm, Multiple Hypothesis for Object Class Disambiguation from Multiple Observations, and evaluate its accuracy and efficiency.