Mobile ground robots operating on unstructured terrain must predict which
areas of the environment they are able to pass in order to plan feasible paths.
We address traversability estimation as a heightmap classification problem: we
build a convolutional neural network that, given an image representing the
heightmap of a terrain patch, predicts whether the robot will be able to
traverse such patch from left to right. The classifier is trained for a
specific robot model (wheeled, tracked, legged, snake-like) using simulation
data on procedurally generated training terrains; the trained classifier can be
applied to unseen large heightmaps to yield oriented traversability maps, and
then plan traversable paths. We extensively evaluate the approach in simulation
on six real-world elevation datasets, and run a real-robot validation in one
indoor and one outdoor environment.

该研究论文介绍了一种基于卷积神经网络的高程图分类方法，在模拟环境中训练唯一的分类器来预测机器人在不同地形上的通过性，并在室内和室外环境中进行真实机器人验证。

从模拟中学习地面可通行性

Learning Ground Traversability from Simulations

We present a learning-based mapless motion planner by taking the sparse
10-dimensional range findings and the target position with respect to the
mobile robot coordinate frame as input and the continuous steering commands as
output. Traditional motion planners for mobile ground robots with a laser range
sensor mostly depend on the obstacle map of the navigation environment where
both the highly precise laser sensor and the obstacle map building work of the
environment are indispensable. We show that, through an asynchronous deep
reinforcement learning method, a mapless motion planner can be trained
end-to-end without any manually designed features and prior demonstrations. The
trained planner can be directly applied in unseen virtual and real
environments. The experiments show that the proposed mapless motion planner can
navigate the nonholonomic mobile robot to the desired targets without colliding
with any obstacles.

通过深度强化学习方法训练，可直接应用于未知的虚拟和实际环境中的地面移动机器人的无地图运动规划器。