DICP：多普勒迭代最近点算法
DICP: Doppler Iterative Closest Point Algorithm
摘要：
本文提出了一种新的点云配准算法：多普勒ICP算法。仅依赖于几何形状或其他特征的ICP的现有变体通常无法在具有非独特特征和/或重复几何结构（诸如走廊、隧道、高速公路和桥梁）的场景中正确地估计传感器的运动。我们提出了一个新的多普勒速度目标函数，利用每个点的多普勒测量和传感器的当前运动估计的兼容性。我们联合优化多普勒速度目标函数和几何目标函数，即使在特征否认的环境中，也充分约束了点云对齐问题。此外，用于对准的对应匹配通过从动态目标修剪掉点来改进，动态目标通常使ICP解决方案降级。我们评估我们的方法从真实的传感器和模拟收集的数据。我们的研究结果表明，与仅依赖于几何残差的经典点到平面ICP相比，我们的方法在配准精度方面取得了显着的改善，并且平均收敛速度更快。
英文摘要：
In this paper, we present a novel algorithm for point cloud registration for range sensors capable of measuring per-return instantaneous radial velocity: Doppler ICP. Existing variants of ICP that solely rely on geometry or other features generally fail to estimate the motion of the sensor correctly in scenarios that have non-distinctive features and/or repetitive geometric structures such as hallways, tunnels, highways, and bridges. We propose a new Doppler velocity objective function that exploits the compatibility of each point's Doppler measurement and the sensor's current motion estimate. We jointly optimize the Doppler velocity objective function and the geometric objective function which sufficiently constrains the point cloud alignment problem even in feature-denied environments. Furthermore, the correspondence matches used for the alignment are improved by pruning away the points from dynamic targets which generally degrade the ICP solution. We evaluate our method on data collected from real sensors and from simulation. Our results show that with the added Doppler velocity residual terms, our method achieves a significant improvement in registration accuracy along with faster convergence, on average, when compared to classical point-to-plane ICP that solely relies on geometric residuals.
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