We propose a learning-based method to reconstruct the local terrain for locomotion with a mobile robot traversing urban environments. Using a stream of depth measurements from the onboard cameras and the robot's trajectory, the algorithm estimates the topography in the robot's vicinity. The raw measurements from these cameras are noisy and only provide partial and occluded observations that in many cases do not show the terrain the robot stands on. Therefore, we propose a 3D reconstruction model that faithfully reconstructs the scene, despite the noisy measurements and large amounts of missing data coming from the blind spots of the camera arrangement. The model consists of a 4D fully convolutional network on point clouds that learns the geometric priors to complete the scene from the context and an auto-regressive feedback to leverage spatio-temporal consistency and use evidence from the past. The network can be solely trained with synthetic data, and due to extensive augmentation, it is robust in the real world, as shown in the validation on a quadrupedal robot, ANYmal, traversing challenging settings. We run the pipeline on the robot's onboard low-power computer using an efficient sparse tensor implementation and show that the proposed method outperforms classical map representations.
翻译:我们提议了一个基于学习的方法,用移动机器人横跨城市环境来重建当地地形,以进行巡回移动。使用机上摄像机和机器人轨道的深度测量流,算法估计机器人周围的地形。这些相机的原始测量是吵闹的,只能提供部分和隐蔽的观测,在许多情况下,这些测量没有显示机器人所处的地形。因此,我们提议了一个3D重建模型,忠实地重建现场,尽管摄像安排盲点产生的测量和大量数据缺失。模型包括点云上的4D全演动网络,从上下文中学习完成场景的几何前程,以及自动反向反馈,以利用波形-时空一致性并使用过去的证据。这个网络可以仅仅通过合成数据来训练,而且由于广泛的增强,它能够在现实世界中强大起来,正如对一个四分立的机器人,Ammal,Treadersing 挑战性环境的验证所显示的那样。我们在低能量计算机模型上运行管道,使用高效的蒸发式的气压法展示了机上的拟议模型。