Motion estimation from range images in dynamic outdoor scenes

Object-class independent motion estimation from range data is a challenging task. We present here a novel approach that is able to derive a dense motion field based on range images only. We propose to first segment the range image into segments using a recently proposed segmentation criterion. Motion is then estimated segment-wise with full 6 degrees of freedom. To that end, we introduce dynamic mapping, i.e. the accumulation of measurements for moving objects. We show experimentally that the approach is able to deliver a dense motion field which can then be used for object-class independent trajectory estimation.