论文标题
骆驼：学习成本地图使越野驾驶变得容易
CAMEL: Learning Cost-maps Made Easy for Off-road Driving
论文作者
论文摘要
机器人车使用成本图来规划无碰撞路径。与地图中每个单元相关的成本代表了感知的环境信息，这些信息通常是在经过几次反复试验后手动确定的。在越野环境中，由于存在几种类型的功能，将与每个功能相关的成本值进行手工制作是一项挑战。此外，不同手工制作的成本值可能会导致相同环境的不同路径，而不可取的环境。在本文中，我们解决了从感知的稳定车辆路径计划中学习成本映射值的问题。我们使用深度学习方法提出了一个名为“骆驼”的新型框架，该方法通过演示来学习参数，从而为路径规划提供适应性且强大的成本图。骆驼已在诸如Rellis-3D等多模式数据集中接受过培训。骆驼的评估是在越野场景模拟器（MAV）和IISER-B校园的现场数据上进行的。我们还在地面流动站上执行了骆驼的现实实施。结果表明，在非结构化的地形上没有碰撞的情况下，车辆的灵活而强大的运动。
Cost-maps are used by robotic vehicles to plan collision-free paths. The cost associated with each cell in the map represents the sensed environment information which is often determined manually after several trial-and-error efforts. In off-road environments, due to the presence of several types of features, it is challenging to handcraft the cost values associated with each feature. Moreover, different handcrafted cost values can lead to different paths for the same environment which is not desirable. In this paper, we address the problem of learning the cost-map values from the sensed environment for robust vehicle path planning. We propose a novel framework called as CAMEL using deep learning approach that learns the parameters through demonstrations yielding an adaptive and robust cost-map for path planning. CAMEL has been trained on multi-modal datasets such as RELLIS-3D. The evaluation of CAMEL is carried out on an off-road scene simulator (MAVS) and on field data from IISER-B campus. We also perform realworld implementation of CAMEL on a ground rover. The results shows flexible and robust motion of the vehicle without collisions in unstructured terrains.
