December 2022
Thatavarthy V V S S T Ayyappa Swamy received his Master of Science in Electronics and Communication Engineering (ECE). His research work was supervised by Prof. Madhava Krishna. Here’s a summary of his research work on Structure-Aware Monocular Visual-Inertial Navigation of Unmanned Aerial Vehicles (UAVs) in Urban Scenes:
Navigation of Unmanned Aerial Vehicles (UAVs) amongst high-rises in urban environments becomes 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 with inherent piecewise planar structures. Leveraging this inherent structural information for navigating a UAV equipped with a single camera and an IMU is the primary focus of this thesis. We make three contributions that leverage these geometric cues to detect and map these planar structures for obstacle avoidance and path planning.
Our first contribution, Multi-View Planarity Constraints for skyline estimation from UAV images in city scale urban environments, is a three-stage pipeline that brings together several modules combining a data driven monocular plane segmentation network and geometric constraints from 3D vision to showcase a quick reconstruction of planar facades within a few views. We evaluate the efficacy of our pipeline with various constraints and errors from multi-view geometry using ablation studies. We then 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 and motion estimation in Manhattan Scenes, we propose a novel method of pose estimation using line features from three views of a Manhattan Scene. We leverage the vanishing point directions to estimate the 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 line BA using factor graph based cost function. We compare the efficacy of our method with standard line triangulation in synthetic as well as real-world scenes.
Finally, we propose UrbanFly: Uncertainty-Aware Planning for Navigation Amongst High-Rises with Monocular Visual-Inertial SLAM Maps, a sequential convex program based novel trajectory planner tailored for outdoor urban scenes. It uses the sparse point clouds generated by a Monocular Visual Inertial SLAM (VINS) backend to build a cuboid representation of the environment through a data-driven monocular plane segmentation network.