Adaptive Terrain Traversability Prediction based on Multi-Source Transfer Gaussian Processes
Adaptive Terrain Traversability Prediction based on Multi-Source Transfer Gaussian Processes
复制标题
基于多源传递高斯过程的自适应地形通行性预测
DOI：
10.1109/iros51168.2021.9636528
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Takashi Kubota
中科院分区：
文献类型：
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作者：
H. Inotsume;Takashi Kubota
这项研究解决了预测越野车的地形通过性的挑战。当越野车在粗糙的地形或松散材料的斜坡上行驶时，准确预测地形的可通行性以实现高效行驶并避免关键的机动性风险至关重要。然而，可穿越性的预测是具有挑战性的，特别是对于可能有风险的地形的预测，因为对于这样的地形，可用的穿越数据是有限的或不存在的。针对这一局限性，提出了一种基于多源传输高斯过程回归（MS-TGPR）的自适应地形可通过性预测方法。该方法利用目标环境中低风险地形的有限数据，通过利用过去在多种类型的地形表面上的遍历经验来提高预测精度。使用各种地形表面和几何形状的滑动数据集证明了所提出方法的有效性。
This study addresses the challenge of predicting the terrain traversability of off-road vehicles. When an off-road vehicle is operated on rough terrains or slopes of unconsolidated materials, it is crucial to accurately predict terrain traversability for efficient operations and to avoid critical mobility risks. However, the prediction of traversability is challenging, especially for the prediction of possibly risky terrains because for such terrains, the traverse data available is either limited or non-existent. To address this limitation, this study proposes an adaptive terrain traversability prediction method based on the multi-source transfer Gaussian process regression (MS-TGPR). The proposed method utilizes limited data available on low risk terrains of the target environment to enhance the prediction accuracy by leveraging past traverse experiences on multiple types of terrain surfaces. The effectiveness of the proposed method is demonstrated using a slip dataset of various terrain surfaces and geometries.