论文标题
深贝叶斯ICP协方差估计
Deep Bayesian ICP Covariance Estimation
论文作者
论文摘要
迭代最接近点（ICP）点云配准算法的协方差估计对于状态估计和传感器融合目的至关重要。我们认为，从传感器噪声到场景几何形状，ICP的主要错误来源是输入数据本身。从最新学习的点云中受益，我们提出了一种数据驱动的方法，以学习ICP的错误模型。我们使用差异性贝叶斯方法来估计数据依赖性异质的不确定性和认知不确定性。系统评估是在不同数据集上的LiDAR探光仪上进行的，与最新情况相比，强调了良好的结果。
Covariance estimation for the Iterative Closest Point (ICP) point cloud registration algorithm is essential for state estimation and sensor fusion purposes. We argue that a major source of error for ICP is in the input data itself, from the sensor noise to the scene geometry. Benefiting from recent developments in deep learning for point clouds, we propose a data-driven approach to learn an error model for ICP. We estimate covariances modeling data-dependent heteroscedastic aleatoric uncertainty, and epistemic uncertainty using a variational Bayesian approach. The system evaluation is performed on LiDAR odometry on different datasets, highlighting good results in comparison to the state of the art.
