Multiple-object detection in natural scenes with multiple-view expectation maximization clustering

Mobile robots and robot teams can leverage multiple views of a scene to improve the accuracy of their maps. However non-uniform noise persists even when each sensor\´s pose is known, and the uncertain correspondence between detections from different views complicates easy "multiple view object detection." We present an algorithm based on expectation/maximization (EM) clustering that permits a principled fusion of the views without requiring an explicit correspondence search. We demonstrate the use of this algorithm to improve mapping performance of robots in simulation and in the field.