基于ICP的车辆无监督运动估计

Tom Roussel, T. Tuytelaars, L. V. Eycken
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引用次数: 0

摘要

预测动态对象的运动对于在避免碰撞的同时在环境中导航做出智能决策至关重要。在这项工作中,我们提出了一个CNN模型,该模型使用单眼图像序列估计物体的3D运动。通过使用迭代最近点(ICP)在不同时间点对齐对象的点云,我们可以在不使用任何手动注释的情况下训练该模型。我们将我们的无监督方法与在KITTI跟踪数据集上使用地面真相监督训练的模型进行比较。我们通过在更大的数据集上训练我们的模型来进一步改进我们的模型,否则由于缺乏真实数据,这是不可能的。我们还将我们的方法与使用简单跟踪方案估计运动的3D物体检测器进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Unsupervised Motion Estimation of Vehicles Using ICP
Anticipating the motion of dynamic objects is critical for making intelligent decisions navigating through an environment while avoiding collisions. In this work, we propose a CNN model that estimates 3D motion of objects using sequences of monocular images. We show that we can train this model without using any manual annotations by using Iterative Closest Points (ICP) to align pointclouds of an object at different points in time. We compare our unsupervised approach to a model that was trained using ground truth supervision, on the KITTI tracking dataset. We further improve our model by training our model on a larger dataset, which would otherwise not be possible due to the lack of ground truth data. We also compare our approach with a 3D object detector that estimates motion using a simple tracking scheme.
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