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VIO-GO:优化基于事件的SLAM参数,在高动态范围场景中提供强大的性能
Saber Sakhrieh1, Abhilasha Singh1, Jinane Mounsef1
1Electrical Engineering and Computing Sciences Department, Rochester Institute of Technology, Dubai, United Arab Emirates.
Frontiers in robotics and AI
|October 6, 2025
概括
这项研究通过使用事件摄像头和一种基于VIO梯度的优化 (VIO-GO) 方法来增强工业4.0机器人中的视觉惯性计数 (VIO). 在具有挑战性的工业环境中,VIO-GO显著提高了定位精度.
科学领域:
- 机器人和自动化机器人与自动化
- 计算机视觉 计算机视觉
- 传感器融合式传感器
背景情况:
- 工业4.0机器人在动态,低光环境中面临着挑战.
- 现有的视觉惯性测距 (VIO) 系统在这些条件下难以保证可靠性.
- 事件摄像机在具有挑战性的工业环境中提供了改进传感的潜力.
研究的目的:
- 增强VIO系统,在动态和低光工业4.0环境中提供强大的性能.
- 为事件同时定位和映射 (SLAM) 参数引入一种新的优化方法.
- 在复杂的工业场景中实现精确可靠的机器人定位和映射.
主要方法:
- 生物启发事件摄像机与传统视频和惯性数据集成,用于状态估计.
- 开发使用批量梯度下降 (BGD) 的基于VIO梯度优化 (VIO-GO) 方法.
- 为事件SLAM使用运动补偿图像来表示事件数据的自动参数调整.
主要成果:
- 与固定参数方法相比,平均位置误差 (MPE) 得到了60%的改进.
- 对于精确的VIO性能,VIO-GO始终确定了最佳参数.
- 观察到MPE减少了24%,而参数复杂性增加 (VIO-GO8与VIO-GO2).
结论:
- 拟议的VIO-GO方法有效地优化事件SLAM参数,用于实际应用.
- 增强的VIO系统在具有挑战性的工业环境中展示了强大而精确的本地化能力.
- 这种方法具有可扩展性,适用于工业4.0.0中的自适应机器人系统.
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