Layout aware visual tracking and mapping

Nowadays real time visual Simultaneous Localization And Mapping (SLAM) algorithms exist and rely on consistent measurements across multiple views. In indoor environments, where majority of robot's activity takes place, severe occlusions can occur, e.g., when turning around a corner or moving from one room to another. In these situations, SLAM algorithms can not establish correspondences across views, which leads to failures in camera localization or map construction. This work takes advantage of the recent scene box layout descriptor to make the above mentioned SLAM systems occlusion aware. This room box reasoning helps the sequential tracker to reason about possible occlusions and therefore look for matches in only potentially visible features instead of the entire map. This increases the life of the tracker, as it does not consider itself lost under the occlusion state. Additionally, focusing on the potentially visible portion of the map, i.e., the current room features, it improves the computational efficiency without compromising the accuracy. Finally, this room level reasoning helps in better image selection for bundle adjustment. The image bundle coming from the same room has little occlusion, which leads to better dense reconstruction. We demonstrate the superior performance of layout aware SLAM on several long monocular sequences acquired in difficult indoor situations, specifically in a room-room transition and turning around a corner.