无人平台复杂地形探测的视触融合方法

为提高无人平台在复杂环境中的地形探测能力以及解决在小样本数据下识别地形困难的问题，提出了一种无人平台复杂地形探测的视触融合方法。在原始宽度学习的基础上，建立了多模态级联特征节点宽度学习框架。首先进行触觉和视觉初步特征提取和融合特征提取，随后将融合特征矩阵经宽度学习分类器得到地形识别的结果。最后，在自建的视觉-触觉地形 (V-T2)数据集进行了实验验证。结果表明，相比于传统的融合算法，提出的融合算法有很好的准确性和鲁棒性，为无人平台地形探测提供了有效的策略。

Visual and Touch Fusion Method for Complex Terrain Detection on Unmanned Platform

In order to improve the terrain detection ability of unmanned platform in a complex environment and solve the problem of terrain recognition difficulty in small sample data, visual and touch fusion method for complex terrain detection on the unmanned platform is proposed. According to original broad learning, a multi-modal cascade feature nodes broad learning framework is established. First, extracting visual and tactile initial features, and then the fusion features are extracted. Next, the fusion feature matrix is classified by the broad learning classifier to obtain the classification results. Finally, experimental verification is carried out on the self-built data set named visual-touch terrain (V-T2). The results show that compared with the traditional fusion algorithms, the proposed fusion algorithm has good accuracy and robustness, and it provides an effective strategy for robot terrain detection.