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空间时空校准和地面真相估计用于扩展现实中高精度SLAM基准测试
IEEE transactions on visualization and computer graphics
|October 2, 2025
概括
本研究引入了一种新的方法,用于在扩展现实 (XR) 中精确的同时定位和映射 (SLAM) 基准测试. 它通过整合IMU数据来克服移动捕捉的局限性,提高SLAM算法评估,从而提高了地面真相的准确性.
科学领域:
- 机器人和计算机视觉 机器人和计算机视觉
- 扩展现实 (XR) 技术
- 传感器的融合和校准.
背景情况:
- 同时定位和映射 (SLAM) 对于沉浸式扩展现实 (XR) 体验至关重要.
- 基于标记器的光学运动捕捉 (MoCap) 是用于SLAM基准测试生成地面真相 (GT) 的标准.
- 基于MoCap的GT精度受到时空校准和测量动的限制,影响了关键的XR指标.
研究的目的:
- 在XR应用中开发一种用于精确SLAM基准测试的新方法.
- 为了解决基于MoCap的基层真相生成的局限性.
- 为了能够更准确地评估SLAM算法以提高XR沉浸度.
主要方法:
- 建议使用连续时间最大概率估计器.
- 集成了辅助惯性测量单元 (IMU) 数据,以补偿MoCap动.
- 基于螺杆一致性约束的可变时间同步和姿势余量确保了精确的时空校准.
主要成果:
- 拟议的方法在SLAM基准准测试精度方面明显优于现有的方法.
- 获得的精度足以对最先进的SLAM算法进行全面评估.
- 实用性通过对领先的XR设备和SLAM算法的基准测试来验证.
结论:
- 新型估计器提高了SLAM对XR应用的基准测试的准确性.
- 该方法有效地减轻了MoCap的限制,改善了诸如旋转误差和动等关键指标.
- 公共可用的代码促进了XR SLAM的进一步研究和开发.
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