可视化分析ICP变体在激光里程计中的表现，提升动态物体与盲区处理能力。
A visual study of ICP variants for Lidar Odometry
- 通过二维可视化揭示ICP多维目标函数，直观分析不同变体性能
- 提出新方法有效过滤动态物体并解决车辆自身盲区问题
- 适合自动驾驶定位与传感器融合研究者参考
基于激光雷达的里程计是估计移动车辆自身位姿的前沿方法。众多实现采用迭代最近点（ICP）算法的变体。真实场景中的动态物体、非重叠区域及传感器噪声会降低ICP精度。本文基于一种新提出的可视化方法，将ICP的多维目标函数映射至二维空间，以直观呈现其优化过程。利用该方法，系统评估了多种ICP变体在激光里程计中的表现。此外，本文提出一种新策略，用于过滤动态物体并解决车辆自身的视觉盲区问题，显著提升定位鲁棒性。
原文摘要 · Abstract (English)
Odometry with lidar sensors is a state-of-the-art method to estimate the ego pose of a moving vehicle. Many implementations of lidar odometry use variants of the Iterative Closest Point (ICP) algorithm. Real-world effects such as dynamic objects, non-overlapping areas, and sensor noise diminish the accuracy of ICP. We build on a recently proposed method that makes these effects visible by visualizing the multidimensional objective function of ICP in two dimensions. We use this method to study different ICP variants in the context of lidar odometry. In addition, we propose a novel method to filter out dynamic objects and to address the ego blind spot problem.
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