对比小比例与真实车辆的定位算法，发现小车可有效替代真车测试定位系统。
Comparison of Localization Algorithms between Reduced-Scale and Real-Sized Vehicles Using Visual and Inertial Sensors
- 用相机和IMU在缩比车与实车对比测试定位算法
- OpenVINS在缩比车上表现最佳，平均误差最低
- 旋转与平移定位误差差异小，缩比车适合作为测试平台
物理缩小的缩比车辆正被用于加速高级自动驾驶功能的开发。本文研究了缩放对基于视觉与惯性传感器自定位精度的影响。选取了支持ROS2的视觉与视觉-惯性算法（OpenVINS、VINS-Fusion、RTAB-Map），以真实车辆数据集为基准，开展缩比车辆测试驾驶并采集数据。通过与真实车辆数据及地面真值对比，评估各算法的位姿精度。结果显示，所有算法误差范围重叠，但OpenVINS在缩比车上的平均定位误差最低。相较真实车辆，缩比车在平移运动估计上仅有微小差异，而旋转运动估计精度无显著差别，表明缩比车辆可作为自定位算法的有效测试平台。
原文摘要 · Abstract (English)
Physically reduced-scale vehicles are emerging to accelerate the development of advanced automated driving functions. In this paper, we investigate the effects of scaling on self-localization accuracy with visual and visual-inertial algorithms using cameras and an inertial measurement unit (IMU). For this purpose, ROS2-compatible visual and visual-inertial algorithms are selected, and datasets are chosen as a baseline for real-sized vehicles. A test drive is conducted to record data of reduced-scale vehicles. We compare the selected localization algorithms, OpenVINS, VINS-Fusion, and RTAB-Map, in terms of their pose accuracy against the ground-truth and against data from real-sized vehicles. When comparing the implementation of the selected localization algorithms to real-sized vehicles, OpenVINS has the lowest average localization error. Although all selected localization algorithms have overlapping error ranges, OpenVINS also performs best when applied to a reduced-scale vehicle. When reduced-scale vehicles were compared to real-sized vehicles, minor differences were found in translational vehicle motion estimation accuracy. However, no significant differences were found when comparing the estimation accuracy of rotational vehicle motion, allowing RSVRs to be used as testing platforms for self-localization algorithms.
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