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Improved 3D Perception based on Color Monocular Camera for MAV exploiting Image Semantic Segmentation

In this paper, we propose an improved 3D perception for MAV based on color monocular camera. We exploit the semantic segmentation of scene color images to derive semantic and geometric constraints on the 2D keypoints and 3D reconstruction. The high-level components of the system are ego-motion estimation of the drone and sparse 3D reconstruction and segmentation of the explored environment. The ego-motion and 3D structure components are highly coupled and any significant improvement in either one greatly influences the other one. The proposed method aims at reducing the outliers that come from dynamic objects by avoiding extraction of keypoints from entities that could potentially exhibit dynamic behavior (cars, trucks, pedestrians, etc.) exploiting the semantic segmentation of the image. Also, we derive a set of semantic and geometric constraints which improve the 3D reconstruction. The resulting 3D point cloud is semantically segmented by transfering the semantic class of the originating points from which it was triangulated. Ultimately, all these improvements have a great impact on the speed-up and accuracy of the optimization method used, in our case windowed bundle adjustment.