论文标题
对于具有语义线索的行星流浪者，可以预测视线的地形机械性能
Predicting Terrain Mechanical Properties in Sight for Planetary Rovers with Semantic Clues
论文作者
论文摘要
非几何流动性危害，例如漫游者滑倒和下沉，对昂贵的行星任务构成了巨大挑战，与地形的机械性能密切相关。漫游者估计地形机械性能的原位本体感受过程需要体验到不同的滑动和下沉，并且对未传播区域无助。本文提议预测距离内视觉的地形机械性能，该特性将传感范围扩展到整个视图，并在计划阶段部分停止潜在的滑移和下沉危害。一种基于语义的方法旨在预测与语义线索相关的两个阶段的地形的轴承和剪切特性。以前的分割阶段段的地形具有轻加权网络，该网络有望在有竞争力的93％精度和高召回率上使用96％以上的船上，而后者的推理阶段则基于人类样的推断原理以定量方式预测地形性能。在多个测试途径中的预测结果为12.5％和10.8％，并有助于计划适当的策略，以避免遭受非几何危害。
Non-geometric mobility hazards such as rover slippage and sinkage posing great challenges to costly planetary missions are closely related to the mechanical properties of terrain. In-situ proprioceptive processes for rovers to estimate terrain mechanical properties need to experience different slip as well as sinkage and are helpless to untraversed regions. This paper proposes to predict terrain mechanical properties with vision in the distance, which expands the sensing range to the whole view and can partly halt potential slippage and sinkage hazards in the planning stage. A semantic-based method is designed to predict bearing and shearing properties of terrain in two stages connected with semantic clues. The former segmentation phase segments terrain with a light-weighted network promising to be applied onboard with competitive 93% accuracy and high recall rate over 96%, while the latter inference phase predicts terrain properties in a quantitative manner based on human-like inference principles. The prediction results in several test routes are 12.5% and 10.8% in full-scale error and help to plan appropriate strategies to avoid suffering non-geometric hazards.
