3D visual-inertial odometry and autonomous mobile robot exploration with learned map prediction

2D and 3D scene reconstruction are important topics in the field of robotics and computer vision. Mobile robots require a model of the environment to perform navigational tasks, and model acquisition is a useful application in itself . This thesis presents a) A 3D odometry and mapping system producing metric scale map and pose estimates using a minimal sensor-suite b) An autonomous ground robot for 2D mapping of an unknown environment using learned map prediction. The first application proposes a direct visual-inertial odometry method working with a monocular camera. This system builds upon the state-of-the-art in direct vision-only odometry. It demonstrates superior system robustness and camera tracking accuracy compared to the original method. Furthermore, the system is able to produce a 3D map in metric scale, addressing the well known scale ambiguity inherent in monocular SLAM systems.The second application demonstrates an autonomous ground robot capable of exploring unknown indoor environments for reconstructing their 2D maps. This method combines the strengths of traditional information-theoretic approaches towards solving this problem and more recent deep learning techniques. Specifically, it employs a state-of-the-art generative neural network to predict unknown regions of a partially explored map, and uses the prediction to enhance the exploration in an information-theoretic manner. The system is evaluated against traditional methods in simulation using floor plans of real buildings and demonstrates advantage in terms of exploration efficiency. We retain an advantage over end-to-end learned exploration methods in that the robot's behavior is easily explicable in terms of the predicted map.