论文标题
从自我运动中迈向准确的地面平面正常估计
Towards Accurate Ground Plane Normal Estimation from Ego-Motion
论文作者
论文摘要
在本文中，我们介绍了一种新型的方法，用于对轮式车辆的正常估计。实际上，由于制动和不稳定的道路表面，地面平面被动态更改。结果，车辆姿势，尤其是螺距角度，从微妙到明显的振荡。因此，估计地面平面正常是有意义的，因为它可以编码以提高各种自动驾驶任务的鲁棒性（例如3D对象检测，道路表面重建和轨迹计划）。我们提出的方法仅将探针仪用作输入，并实时估算准确的接地平面正常向量。特别是，它充分利用了自我姿势进程（Ego-Motion）及其附近地面平面之间的基本联系。基于此基础，不变的扩展卡尔曼过滤器（IEKF）旨在估计传感器坐标中的正常向量。因此，我们提出的方法是简单而有效的，并且支持基于摄像机和惯性的探针仪算法。它的可用性和鲁棒性的明显改善通过公共数据集的多个实验得到了验证。例如，我们在KITTI数据集上实现了最新的准确性，估计的向量误差为0.39°。我们的代码可在github.com/manymuch/ground_normal_filter上找到。
In this paper, we introduce a novel approach for ground plane normal estimation of wheeled vehicles. In practice, the ground plane is dynamically changed due to braking and unstable road surface. As a result, the vehicle pose, especially the pitch angle, is oscillating from subtle to obvious. Thus, estimating ground plane normal is meaningful since it can be encoded to improve the robustness of various autonomous driving tasks (e.g., 3D object detection, road surface reconstruction, and trajectory planning). Our proposed method only uses odometry as input and estimates accurate ground plane normal vectors in real time. Particularly, it fully utilizes the underlying connection between the ego pose odometry (ego-motion) and its nearby ground plane. Built on that, an Invariant Extended Kalman Filter (IEKF) is designed to estimate the normal vector in the sensor's coordinate. Thus, our proposed method is simple yet efficient and supports both camera- and inertial-based odometry algorithms. Its usability and the marked improvement of robustness are validated through multiple experiments on public datasets. For instance, we achieve state-of-the-art accuracy on KITTI dataset with the estimated vector error of 0.39°. Our code is available at github.com/manymuch/ground_normal_filter.
