The outdoor navigation capabilities of ground robots have improved significantly in recent years, opening up new potential applications in a variety of settings. Cost-based representations of the environment are frequently used in the path planning domain to obtain an optimized path based on various objectives, such as traversal time or energy consumption. However, obtaining such cost representations is still cumbersome, particularly in outdoor settings with diverse terrain types and slope angles. In this paper, we address this problem by using a data-driven approach to develop a cost representation for various outdoor terrain types that supports two optimization objectives, namely energy consumption and traversal time. We train a supervised machine learning model whose inputs consists of extracted environment data along a path and whose outputs are the predicted energy consumption and traversal time. The model is based on a ResNet neural network architecture and trained using field-recorded data. The error of the proposed method on different types of terrain is within 11\% of the ground truth data. To show that it performs and generalizes better than currently existing approaches on various types of terrain, a comparison to a baseline method is made.

翻译：近年来，地面机器人的室外导航能力显著提升，为其在多种场景下的新应用开辟了可能性。在路径规划领域，基于成本的環境表徵常被用于获取针对不同目标（如通行时间或能耗）的优化路径。然而，获取此类成本表徵仍较为繁琐，特别是在具有多种地形类型和坡度的室外环境中。本文通过数据驱动方法解决该问题，针对多种室外地形类型构建支持能耗与通行时间双优化目标的成本表徵。我们训练了一个监督式机器学习模型，其输入为路径上提取的环境数据，输出为预测的能耗与通行时间。该模型基于ResNet神经网络架构，并使用实地采集数据进行训练。所提方法在不同地形上的误差均控制在真实数据的11%以内。通过与基准方法比较，验证了该方法在多地形场景下相比现有方法具有更优的性能与泛化能力。