来自开放式户外无人机数据集的大尺寸视频的运动高效结构
Ruilin Xiang1, Jiagang Chen1, Shunping Ji1
1School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China.
Sensors (Basel, Switzerland)
|May 25, 2024
概括
本研究引入了一种新的运动结构 (SfM) 方法,用于高分辨率无人机 (UAV) 视频,提高大规模绘图的效率和定位精度.
科学领域:
- 计算机视觉 计算机视觉
- 机器人技术 机器人技术 机器人技术
- 地理空间分析是什么
背景情况:
- 现代无人飞行器 (UAV) 产生高分辨率的视频数据,挑战现有的运动结构 (SfM) 和用于更小,更低分辨率场景的同时定位和映射 (SLAM) 算法.
- 当前的SfM/SLAM方法在处理大规模,高分辨率无人机视频时面临计算成本问题.
研究的目的:
- 开发一种高效准确的基于视频的SfM方法,适用于高分辨率的大型无人机视频.
- 为解决处理大量无人机视频数据以进行3D重建和本地化所带来的计算挑战.
主要方法:
- 使用视觉SLAM (VSLAM) 系统,有效地从下面采样的视频中提取关键和关键点.
- 实现了一种新的两步关键点调整技术,以在原始视频尺度上完善现有关键点.
- 采用旋转平均限定的全球捆绑调整 (BA) 调整姿势和结构.
主要成果:
- 与其他方法相比,新收集的大型数据集的平均效率提高了100%,而EuRoc数据集的平均效率提高了45%.
- 与最先进的SLAM和SfM技术相比,显示出卓越的本地化准确性.
- 引入了一个大规模的 (3840 × 2160) 户外视频数据集,具有毫米精度的地面控制点.
结论:
- 拟议的SfM方法为处理高分辨率无人机视频数据提供了显著的效率和准确性收益.
- 新的数据集为推进大规模SLAM和SfM研究提供了宝贵的资源.
- 这项工作有助于从空中图像中进行更强大,更可扩展的3D重建和本地化.
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