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 highlevel components of the system are egomotion estimation of the drone and sparse 3D reconstruction and segmentation of the explored environment. The egomotion 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 speedup and accuracy of the optimization method used, in our case windowed bundle adjustment.

Semantic Segmentation Learning for Autonomous UAVs using Simulators and Real Data Deep learning requires large amounts of data for training models. For the task of semantic segmentation, manual annotation is timeconsuming and difficult. With the recent advances in game engines, simulators have become more popular as they can instantly generate ground truth data for multiple sensors. In this paper, we make a thorough survey of the most recent and popular simulators and synthetic datasets, exploring solutions for semantic segmentation on images taken from drones. We also propose an extension of the CARLA simulator by introducing an aerial camera. We evaluate a deep learning model trained on both synthetic and real data and present a new dataset which comprises both.