Generalized dynamic object removal for dense stereo vision based scene mapping using synthesised optical flow

Mapping an ever changing urban environment is a challenging task as we are generally interested in mapping the static scene and not the dynamic objects, such as cars and people. We propose a novel approach to the problem of dynamic object removal within stereo based scene mapping that is both independent of the underlying stereo approach in use and applicable to varying object and camera motion. By leveraging stereo odometry, to recover camera motion in scene space, and stereo disparity, to recover synthesised optic flow over the same pixel space, we isolate regions of inconsistency in depth and image intensity. This allows us to illustrate robust dynamic object removal within the stereo mapping sequence. We show results covering objects with a range of motion dynamics and sizes of those typically observed in an urban environment.