在Crowd-VI:一个现实的视觉-惯性数据集,用于评估同时定位和绘制在室内行人丰富空间的人类导航的地图
Marziyeh Bamdad1,2, Hans-Peter Hutter1, Alireza Darvishy1
1Institute of Computer Science, Zurich University of Applied Sciences, 8400 Winterthur, Switzerland.
Sensors (Basel, Switzerland)
|January 8, 2025
概括
新的InCrowd-VI数据集通过在拥挤的室内空间测试同时定位和映射 (SLAM) 来帮助视力受损的导航. 当前的SLAM系统正在与现实世界的挑战作斗争,突出了需要更好的算法.
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
- 人与计算机的交互
背景情况:
- 同时定位和映射 (SLAM) 对于视力受损的导航至关重要.
- 现有的SLAM数据集对于拥挤的室内环境缺乏现实性.
- 对于富有行人空间的强大的SLAM的开发受到数据限制的阻碍.
研究的目的:
- 介绍InCrowd-VI,这是一个用于室内,富有行人环境的人类导航的新型视觉惯性数据集.
- 为评估SLAM和视觉测距 (VO) 算法提供一个现实的基准.
- 解决当前数据集在捕捉复杂的导航挑战方面的局限性.
主要方法:
- 使用Meta Aria项目眼镜记录了58个序列 (5公里,1.5小时),捕获了RGB,立体图像和IMU数据.
- 数据集包括现实的挑战:行人堵塞,不同的人群密度,复杂的布局和照明变化.
- 为每个序列提供了地面真实轨迹 (精度约2厘米) 和半密集的3D点云.
主要成果:
- 最先进的VO和SLAM算法在InCrowd-VI.上显示了严重的性能限制.
- 系统在具有挑战性的条件下超过了定位精度 (0.5米) 和漂移值 (1%).
- 经典方法的流动率为5-10%,而深度学习方法缺乏实时处理速度.
结论:
- InCrowd-VI数据集揭示了当前用于视力受损导航的SLAM算法的显著性能差距.
- 该数据集对于在复杂的室内环境中推进SLAM研究至关重要.
- 需要进一步开发实时,准确的SLAM系统,能够应对现实世界的导航挑战.
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