AI & ML
Reflection-Based Relative Localization for Cooperative UAV Teams Using Active Markers
arXiv cs.RO0 views
arXiv:2511.17166v3 Announce Type: replace Abstract: Reflections of active markers in the environment are a common source of ambiguity in onboard visual relative localization. This work presents a novel approach that exploits these typically unwanted reflections for onboard relative localization in heterogeneous multi-UAV teams operating over specular, near-planar surfaces such as calm water and flat indoor floors. Using only a camera, active markers, and a single-beam rangefinder, the method requires no prior knowledge of robot size, marker configuration, or surface reflectance properties, and propagates the uncertainty introduced by surface irregularities, including those of dynamic water surfaces. We further characterize, analytically and experimentally, how surface tilt biases the estimate, and identify the slope range over which the level-surface assumption holds. Indoor multi-UAV and outdoor experiments validate the localization accuracy, demonstrating reliable operation across varying lighting conditions and greater effective range than state-of-the-art vision-based active-marker methods, with higher accuracy beyond 30 m. The project website with the video is available under the following link: https://timlakemann.github.io/reflection-based-relative-localization/.
Source:https://arxiv.org/abs/2511.17166PreviousMultilaminate piezoelectric polymer actuators for insect-scale softrobotsNextAlignDrive: Aligned Lateral-Longitudinal Planning for End-to-End Autonomous Driving
Related posts
The world has a new plan to save nature, but it still needs the money
0Ars Technica

Could data center backlash flip Florida?
0The Verge

Vivo’s first Pro Max phone proves its Ultra is still in a league of its own
0The Verge

Why Peacock is making a huge bet on YouTube
0The Verge
Ninja’s first-ever microwave just replaced two of my kitchen appliances at once
0TechCrunch
Marvell and Lumentum sign definitive 115 billion dollars all-stock merger to build an AI-connectivity leader
0dev.to