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Structure-Aware Monocular Visual-Inertial Navigation of UnmannedAerial Vehicles (UAVs) in Urban ScenesAuthor: Thatavarthy VVSST Ayyappa Swamy 2019702011 Date: 2022-11-29 Report no: IIIT/TH/2022/150 Advisor:Madhava Krishna AbstractNavigation of Unmanned Aerial Vehicles (UAVs) amongst high-rises in urban environments be-comes inevitable due to increasing demand for various applications in Urban Air Mobility (UAM).Moreover, these city scale urban environments are populated with tall buildings and skyscrapers withinherent piecewise planar structures. Leveraging this inherent structural information for navigating aUAV equipped with a single camera and an IMU is the primary focus of this thesis. We make threecontributions that leverage these geometric cues to detect and map these planar structures for obstacleavoidance and path planning.Our first contribution,Multi-View Planarity Constraints for skyline estimation from UAV im-ages in city scale urban environments, is a three-stage pipeline that brings together several modulescombining a data-driven monocular plane segmentation network and geometric constraints from 3D vi-sion to showcase a quick reconstruction of planar facades within a few views. We evaluate the efficacyof our pipeline with various constraints and errors from multi-view geometry using ablation studies. Wethen retrieve the skyline of the buildings in synthetic as well as real-world scenes.In our second contribution,A new geometric approach for three view line reconstruction andmotion estimation in Manhattan Scenes, we propose a novel method of pose estimation using linefeatures from three views of a Manhattan Scene. We leverage the vanishing point directions to estimatethe relative rotations as well as to fix the 3D line direction. In consequence we build a constraints matrix,which has the relative translations and 3D line depth as its null space. We then perform 1-parameter lineBA using factor graph based cost function. We compare the efficacy of our method with standard linetriangulation in synthetic as well as real-world scenes.Finally, we proposeUrbanFly: Uncertainty-Aware Planning for Navigation Amongst High-Rises with Monocular Visual-Inertial SLAM Maps, a sequential convex program based novel trajec-tory planner tailored for outdoor urban scenes. It uses the sparse point clouds generated by a MonocularVisual Inertial SLAM (VINS) backend to build a cuboid representation of the environment through adata-driven monocular plane segmentation network. Our chosen world model provides faster distancequeries than the more common voxel-grid representation. The trajectory optimizer is initialized based onsafety estimates on a set of randomly drawn trajectories. We perform extensive simulations in a tightlyintegrated perception-control loop to validate its efficacy. UrbanFly outperforms competing baselinesin metrics such as collision rate, trajectory length, etc., on a high fidelity AirSim simulator augmentedwith synthetic and real-world dataset scenes. Full thesis: pdf Centre for Robotics |
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