Mesh-Map-Assisted Monocular 3D Localization for UAVs: A Multi-Scale Approach

Unmanned aerial vehicles (UAVs) are increasingly utilized across military, civilian, and agricultural sectors, necessitating accurate and efficient 3D target localization. Traditional 2D detectors lack depth perception, while stereo vision and LiDAR have range-dependent and hardware limitations, respectively. To address these limitations, we propose a monocular 3D localization framework using a prebuilt, georegistered triangular mesh. A two-dimensional grid indexes the mesh, and a progressive multi-scale strategy loads high-resolution blocks for ray–mesh intersection. The nearest intersection is used only for targets supported by, or projectable onto, the mapped surface. Unity experiments give localization RMSEs of 0.0193–0.9229 m; a real UAV field log gives a pooled static-track repeatability RMSE of 0.0849 m. Repeated timing measurements give mean per-query times of 5.08 ms and 1.03 ms for full-resolution and progressive search, respectively.

Authors

Institutions

Publication Details

Journal
Electronics
Published
2026-08-27
DOI
https://doi.org/10.3390/electronics15173850
Primary Topic
Robotics and Sensor-Based Localization
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
article

Mesh-Map-Assisted Monocular 3D Localization for UAVs: A Multi-Scale Approach

Shuaihao Zhang, Mingming Ma, Yanxin Sun, Chao Li et al.
Electronics
Robotics and Sensor-Based Localization
article

Mesh-Map-Assisted Monocular 3D Localization for UAVs: A Multi-Scale Approach

Shuaihao Zhang, Mingming Ma, Yanxin Sun, Chao Li, Lanyu Sun
article en

Abstract

Unmanned aerial vehicles (UAVs) are increasingly utilized across military, civilian, and agricultural sectors, necessitating accurate and efficient 3D target localization. Traditional 2D detectors lack depth perception, while stereo vision and LiDAR have range-dependent and hardware limitations, respectively. To address these limitations, we propose a monocular 3D localization framework using a prebuilt, georegistered triangular mesh. A two-dimensional grid indexes the mesh, and a progressive multi-scale strategy loads high-resolution blocks for ray–mesh intersection. The nearest intersection is used only for targets supported by, or projectable onto, the mapped surface. Unity experiments give localization RMSEs of 0.0193–0.9229 m; a real UAV field log gives a pooled static-track repeatability RMSE of 0.0849 m. Repeated timing measurements give mean per-query times of 5.08 ms and 1.03 ms for full-resolution and progressive search, respectively.

ElectronicsVol. 15(17)
Xidian University (CN), China Academy of Space Technology (CN)
National Natural Science Foundation of China, China Postdoctoral Science Foundation
Zero hunger
Openalex Percentile: Top 7%
Robotics and Sensor-Based Localization
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.