Abstract: Autonomous navigation in visually degraded and unstructured environments, such as darkness, smoke, and rough off-road terrain remains a significant challenge for current robotic systems. RGB cameras fail without illumination, and active sensors such as LiDAR degrade under aerosols and emit signals that are undesirable in sensitive or adversarial scenarios. In contrast, long-wave infrared (thermal) [...]

Abstract: This thesis revisits long-range, image-space planning for off-road navigation and modernizes the classical first-person view (FPV) paradigm by building upon recent advances in perception. It introduces a lightweight depth calibration scheme, analytic configuration-space (C-space) transforms, interpretable frontier selection, and a pixel-space A* planner with validated heuristic soundness. Concretely, we (i) make monocular depth metrically [...]

Abstract: Building large-scale human datasets from multi-view videos is essential for advancing research in human behavior understanding, virtual reality, animation, and robotics. Compared to traditional motion capture systems that rely on physical markers to track motion, vision-based reconstruction not only enables the capture of human motion in unconstrained environments but also avoids altering human appearance [...]

Abstract:  Reliable detection and tracking of power lines is critical for enabling under-wire UAV approach and inductive power-line charging to extend UAV range. However, wires are thin, featureless, and visually ambiguous structures that challenge traditional computer vision methods and degrade depth estimation accuracy. To address these challenges, this thesis presents a fully passive, camera-only multi-wire [...]