论文标题
部分可观测时空混沌系统的无模型预测
DNN Filter for Bias Reduction in Distribution-to-Distribution Scan Matching
论文作者
论文摘要
分布到分布（D2D）点云注册技术（例如正常分布变换（NDT））可以使从非结构化场景采样的点云对齐，并提供其自己的解决方案错误协方差的准确界限 - 安全性导航任务的重要功能。 D2D方法依赖于静态场景的假设，因此易受范围遮挡，自嵌入，移动对象和失真伪像的偏见，因为录制设备在帧之间移动。基于深度学习的方法可以通过放松这些约束来在动态场景中实现更高的准确性，但是，DNN会产生无法解释的解决方案，从安全的角度来看，这可能是有问题的。在本文中，我们提出了一种减小激光雷达点云的方法，以排除违反静态场景假设并在D2D扫描匹配过程中引入错误的体素。我们的方法使用解决方案一致性滤波器 - 识别和抑制D2D贡献与基于PointNet的注册网络的本地估计的体素。我们的结果表明，该技术在注册准确性方面提供了重大好处，并且在包含茂密叶子的场景中特别有用。
Distribution-to-distribution (D2D) point cloud registration techniques such as the Normal Distributions Transform (NDT) can align point clouds sampled from unstructured scenes and provide accurate bounds of their own solution error covariance -- an important feature for safety-of-life navigation tasks. D2D methods rely on the assumption of a static scene and are therefore susceptible to bias from range-shadowing, self-occlusion, moving objects, and distortion artifacts as the recording device moves between frames. Deep Learning-based approaches can achieve higher accuracy in dynamic scenes by relaxing these constraints, however, DNNs produce uninterpretable solutions which can be problematic from a safety perspective. In this paper, we propose a method of down-sampling LIDAR point clouds to exclude voxels that violate the assumption of a static scene and introduce error to the D2D scan matching process. Our approach uses a solution consistency filter -- identifying and suppressing voxels where D2D contributions disagree with local estimates from a PointNet-based registration network. Our results show that this technique provides significant benefits in registration accuracy, and is particularly useful in scenes containing dense foliage.
