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一个新的多传感器非线性紧紧合框架用于复合机器人定位和映射
Lu Chen1,2, Amir Hussain1,2, Yu Liu1
1School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, China.
Sensors (Basel, Switzerland)
|November 27, 2024
概括
这项研究引入了一个新的传感器融合框架,IIVL-LM,用于在具有挑战性的条件下改进机器人的定位和导航. 该系统通过集成多个传感器,提高了准确性和可靠性,特别是在低光环境中.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 计算机视觉 计算机视觉
- 传感器融合式传感器
背景情况:
- 复合机器人在感知方面面临挑战,并因照明变化,干扰和传感器错误而造成估计.
- 现有的系统在动态和低光环境中难以获得准确性和可靠性.
研究的目的:
- 开发一个集成的本地化和导航框架,IIVL-LM,克服环境感知和估计困难.
- 为了提高机器人系统在复杂,可变条件下的强度和精度.
主要方法:
- 提出了一种非线性优化方法,用于密切合的IMU,红外,RGB摄像头和LiDAR数据的数据级融合.
- 开发了一个实时发光率计算模型和特征融合的快速近似方法.
- 在R3LIVE++框架内使用红外摄像头深度信息优化了视觉惯性计数 (VIO) 模块.
主要成果:
- 在具有挑战性的亮度条件下,IIVL-LM系统显示出显著的性能改进,特别是在低光环境中.
- 在模拟的室内救援场景中,在RMSE ATE中平均改善了23%至39% (0.006至0.013).
- 通过对TUM-VI数据集进行比较实验,验证了红外图像融合的关键重要性.
结论:
- 通过保持至少三个传感器的积极参与,IIVL-LM框架显著提高了机器人的稳定性和精度,在未知的和广的环境中.
- 这种综合方法对于在复杂场景中要求高可靠性的应用至关重要,例如室内救援行动.
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