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The accurate terrain classification in real time is of great importance to an autonomous robot working in field, because the robot could avoid non-geometric hazards, adjust control scheme, or improve localization accuracy, with the aid of terrain classification. In this paper, we investigate the vibration-based terrain classification (VTC) in a dynamic environment, and propose a novel learning framework, named DyVTC, which tackles online-collected unlabeled data with concept drift. In the DyVTC framework, the exterior disagreement (ex-disagreement) and interior disagreement (in-disagreement) are proposed novely based on the feature diversity and intrinsic temporal correlation, respectively. Such a disagreement mechanism is utilized to design a pseudo-labeling algorithm, which shows its compelling advantages in extracting key samples and labeling; and consequently, the classification accuracy could be retrieved by incremental learning in a changing environment. Since two sets of features are extracted from frequency and time domain to generate disagreements, we also name the proposed method feature-temporal disagreement adaptation (FTDA). The real-world experiment shows that the proposed DyVTC could reach an accuracy of 89.5%, but the traditional time- and frequency-domain terrain classification methods could only reach 48.8% and 71.5%, respectively, in a dynamic environment.

This paper describes an initial investigation that shows the major impact that moisture and vegetation produce on a soil and how that effect may be measured using a thermal camera. In particular, those two variables influence how the soil compacts and, hence, the traversability of a vehicle. A broad set of experiments, under different weather conditions and with different soils, demonstrate that thermal properties derived from the thermal camera (i.e. thermal inertia) increase when moisture content of sandy soils increases. In addition to that, a relation is observed between thermal inertia and traversability (lower thermal inertia, worse traction; and vice versa). Another key behavior is noticed for vegetated soils, where a similar thermal inertia to wet sand is obtained but with only a third of moisture content. These results may be considered for maximizing traversability over sandy soils with higher thermal inertias, what eventually means higher compaction and safer routes. To the authors’ knowledge, this is the first work addressing the correlation between moisture content and vegetation, and the thermal properties of a soil using a light-weight thermal camera that can be mounted on a mobile robot. [ABSTRACT FROM AUTHOR]

The accurate terrain classification in real time is of great importance to an autonomous robot working in field, because the robot could avoid non-geometric hazards, adjust control scheme, or improve localization accuracy, with the aid of terrain classification. In this paper, we investigate the vibration-based terrain classification (VTC) in a dynamic environment, and propose a novel learning framework, named DyVTC, which tackles online-collected unlabeled data with concept drift. In the DyVTC framework, the exterior disagreement (ex-disagreement) and interior disagreement (in-disagreement) are proposed novely based on the feature diversity and intrinsic temporal correlation, respectively. Such a disagreement mechanism is utilized to design a pseudo-labeling algorithm, which shows its compelling advantages in extracting key samples and labeling; and consequently, the classification accuracy could be retrieved by incremental learning in a changing environment. Since two sets of features are extracted from frequency and time domain to generate disagreements, we also name the proposed method feature-temporal disagreement adaptation (FTDA). The real-world experiment shows that the proposed DyVTC could reach an accuracy of 89.5%, but the traditional time- and frequency-domain terrain classification methods could only reach 48.8% and 71.5%, respectively, in a dynamic environment. [ABSTRACT FROM AUTHOR]

The achievement of robot autonomy has environmental perception as a prerequisite. The hazards rendered from uneven, soft and slippery terrains, which are generally named non-geometric hazards, are another potential threat reducing the traversing efficient, and therefore receiving more and more attention from the robotics community. In the paper, the vibration-based terrain classification (VTC) is investigated by taking a very practical issue, i.e., lack of labels, into consideration. According to the intrinsic temporal correlation existing in the sampled terrain sequence, a modified Laplacian SVM is proposed to utilise the unlabelled data to improve the classification performance. To the best of our knowledge, this is the first paper studying semi-supervised learning problem in robotic terrain classification. The experiment demonstrates that: (1) supervised learning (SVM) achieves a relatively low classification accuracy if given insufficient labels; (2) feature-space homogeneity based semi-supervised learning (traditional Laplacian SVM) cannot improve supervised learning's accuracy, and even makes it worse; (3) feature- and temporal-space based semi-supervised learning (modified Laplacian SVM), which is proposed in the paper, could increase the classification accuracy very significantly. [ABSTRACT FROM AUTHOR]