论文标题
侧向自我 - 车辆控制无监督使用点云
Lateral Ego-Vehicle Control without Supervision using Point Clouds
论文作者
论文摘要
现有的基于视觉的监督侧向车辆控制方法能够将RGB图像直接映射到适当的转向命令。但是，由于培训数据中缺乏故障案件，它们在现实世界中的鲁棒性不足。在本文中，提出了一个用于训练更强大，更可扩展的模型以进行侧向车辆控制的框架。该框架仅需要一个未标记的RGB图像序列。受过训练的模型将点云作为输入，并预测向后续帧推断转向角的侧面偏移。框架姿势反过来从视觉散发器中获得。通过将密集的深度图投影到3D来构想点云。在训练期间，可以生成该点云的任意数量的其他轨迹。这是为了增加模型的鲁棒性。在线实验表明，我们方法的性能优于监督模型。
Existing vision based supervised approaches to lateral vehicle control are capable of directly mapping RGB images to the appropriate steering commands. However, they are prone to suffering from inadequate robustness in real world scenarios due to a lack of failure cases in the training data. In this paper, a framework for training a more robust and scalable model for lateral vehicle control is proposed. The framework only requires an unlabeled sequence of RGB images. The trained model takes a point cloud as input and predicts the lateral offset to a subsequent frame from which the steering angle is inferred. The frame poses are in turn obtained from visual odometry. The point cloud is conceived by projecting dense depth maps into 3D. An arbitrary number of additional trajectories from this point cloud can be generated during training. This is to increase the robustness of the model. Online experiments show that the performance of our method is superior to that of the supervised model.
