TL;DR
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.
A monocular 3D localization framework using a prebuilt, georegistered triangular mesh using a two-dimensional grid indexes the mesh, and a progressive multi-scale strategy loads high-resolution blocks for ray–mesh intersection.
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...
This paper presents a one-stage learning framework that maps monocular roadside-camera images directly to vehicle states in a ground-fixed coordinate frame. Unlike conventional approaches that first detect vehicles in the image plane and subsequently apply geometric post-processing, the proposed method leverages featur...
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 synthesis indicates that LiDAR remains a strong perceptual backbone for UAV autonomy, but that state-of-the-art systems increasingly combine it with inertial, visual, radar, and learning-based components rather than relying on LiDAR in isolation.
Collaborative exploration by unmanned vehicles and drones relies on precise positioning and mapping, as well as efficient path planning. However, inconsistencies in spatial references among cross-platform sensors and the lack of environmental description due to sparse features lead to relative pose estimation drift dur...
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