TL;DR
This is the first work that is able to recover the pose of an UAV at this scale and rate of convergence, while allowing significant seasonal difference between camera observations and map.
The paper proposes a method for visual positioning of unmanned aerial vehicles (UAVs) based on descriptor-based computer vision algorithms. The use of optical navigation is investigated as an auxiliary mechanism under conditions of degradation or total absence of global navigation satellite system (GNSS) signals, where...
A UAV-based system for real-time 3D object localization that integrates a monocular camera, a gimbaled laser rangefinder, and onboard computer vision, thereby enabling fully autonomous operation in outdoor environments and outperforming terrain-aided methods and vision-based stereo approaches.
Quadrotor unmanned aerial vehicles (UAVs) have advantages such as simple structure, flexible manipulation, and strong capabilities of vertical takeoff/landing and hovering, thus being widely applied in various complex environments and tasks. However, their short endurance limits the scope of application. The collaborat...
Autonomous navigation of multi-rotor Unmanned Aerial Vehicles (UAVs) in complex low-altitude environments remains challenging due to severe onboard computational constraints, sensor blind spots, and unpredictable dynamic obstacles. This paper presents a lightweight, integrated autonomous obstacle avoidance system for q...
The uncertainty-aware adaptive vision-LiDAR fusion architecture proposed in this paper is used to track and land a UAV in a GPS-denied region and ensures that the relative state estimation is stable and accurate, by automatically eliminating the effect of unreliable sensor measurements.
The results demonstrate the capability of the proposed UAV platform to perform onboard human detection and GPS-based localization in real-time, providing a basis for UAV-assisted search and rescue applications.
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