概率扫描匹配：基于点云的贝叶斯位姿估计
Probabilistic Scan Matching: Bayesian Pose Estimation from Point Clouds
摘要：
利用高分辨率传感器提供的两个连续点云估计移动平台的位置和姿态变化是自主导航中的关键问题。特别地，扫描匹配算法旨在找到平台的平移和旋转，使得两个点云重合。点云一中的测量与点云二中的测量的关联是扫描匹配固有的问题。现有方法执行非概率数据关联，即，他们假设一个单一的关联假设。这导致在模糊的环境中过度自信的姿态估计和降低的估计精度。我们的概率扫描匹配方法通过考虑所有关联假设及其各自的可能性来解决这个问题。我们制定了一个整体的贝叶斯估计问题的数据关联和姿态估计，并提出了相应的联合因子图。通过在因子图上执行迭代消息传递来计算传感器姿态的近最优最大后验（MAP）估计。我们的数值研究表明，非概率扫描匹配方法，是基于正态分布变换（NDT）和隐式移动最小二乘（IMLS）的性能改善。
英文摘要：
Estimating position and orientation change of a mobile platform from two consecutive point clouds provided by a high-resolution sensor is a key problem in autonomous navigation. In particular, scan matching algorithms aim to find the translation and rotation of the platform such that the two point clouds coincide. The association of measurements in point cloud one with measurements in point cloud two is a problem inherent to scan matching. Existing methods perform non-probabilistic data association, i.e., they assume a single association hypothesis. This leads to overconfident pose estimates and reduced estimation accuracy in ambiguous environments. Our probabilistic scan matching approach addresses this issue by considering all association hypotheses with their respective likelihoods. We formulate a holistic Bayesian estimation problem for both data association and pose estimation and present the corresponding joint factor graph. Near-optimum maximum a posteriori (MAP) estimates of the sensor pose are computed by performing iterative message passing on the factor graph. Our numerical study shows performance improvements compared to non-probabilistic scan matching methods that are based on the normal distributions transform (NDT) and implicit moving least squares (IMLS).
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