Learning to Locate an Object in 3D Space from a Sequence of Camera Images
This paper addresses the problem of determining an object’s 3D location from a sequence of camera images recorded by a 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.