  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.
