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.