Area-wise Augmentation on Segmentation Datasets from 3D Scanned Data Used for Visual Navigation

Visual navigation rely heavily on semantic segmentation outcomes, which is invaluable for practical applications. However, the efficacy of this navigation method is compromised when the accuracy of semantic segmentation falls short. Crucially, the availability of an appropriate dataset containing pixel-wise class labels is imperative for constructing a robust classifier. To alleviate the burden of manual annotation, the authors have endeavored attempt to implement a semi-automatic process for generating a training dataset from 3D scanned data. To enhance the versatility of the approach, the present study introduces augmentation techniques that consider the semantic attributes of images within the target scenario: DMIT and ToD are employed to address color variations caused by seasonal changes lawn growth and fluctuations on the sun’s height, respectively. Experimental results based on images captured during the Tsukuba Challenge, a competition featuring autonomous moving robots in Japan, showed that the proposed methodology substantially enhances classification accuracy, particularly for images taken under conditions different from those during the creation of the 3D model.