Learning to Locate an Object in 3D Space from a Sequence of Camera Images

Abstract

This paper addresses the problem of determining an object's 3D location from a stream of camera images recorded by mobile robot. The approach presented here allows people to "train" robots to recognize specific objects, by presenting it examples of the object to be recognized. A decision tree method is used to learn significant features of the target object from individual camera images. Individual estimates are integrated over time using Bayes rule, into a probabilistic 3D model of the robot's environment. Experimental results illustrate that the method enables a mobile robot to robustly estimate the 3D location of objects from multiple camera images.