A visual navigation method based on results of semantic segmentation showed interesting results in previous researches. The most significant problem of the method is that the moving performance is affected by the segmentation accuracy, which strongly depends on the training data even though SOTA methods are adopted. To create high-quality dataset for this application without huge human efforts, the authors tries to generate datasets for semantic segmentation from a 3D scanned data composed of colored point clouds. In this study, we investigate what kinds of variations are effective to construct a classifier: variation of augmentation considering shadows, shooting angles, and shooting locations. Experimental results using actual images for evaluation and generated dataset for training captured at the course of Tsukuba Challenge, which is the famous competition for autonomous moving robots in Japan, showed that adding shadows in training datasets improved the mIoU but random changes to the shooting angle and location did not always work well. By the result, it is shown that augmentation considering the characteristic of the target environment becomes important for practical use.

Semantic segmentation can provide significant information for a robot to actualize autonomous moving. To increase the classification accuracy of segmentation, appropriate datasets should be prepared, which requires huge human labors. The authors attempt to realize a semi-automatic method that generates two dimensional images having pixel-wise class labels from 3D point clouds obtained by a 3D scanner. In this study, dense point clouds are generated to improve the quality of training samples. Experimental results using data obtained around the course of the Tsukuba Challenge, which is the famous competition for autonomous moving robots in Japan, showed that the dense point clouds effective but appropriate variety of textures should be included in the training samples.

In this research, we propose a method for evaluating images to be inspected using only good images and assuming defective images are unavailable in anticipation of quality inspection applications in food processing facilities and other factories. We propose a discriminator based on a CNN that can evaluate the degree of deviation from good images by treating only good images as training data and assuming a fitness probability distribution. The results show that the proposed discriminator can detect defective products even without prior training data on defective products. The detection accuracy depends on the inspected object and the threshold that defines the deviation, which is comparable to previous studies that require defective images. With adjustable detection thresholds and automatic categorization of defective products, the proposed method is expected to be flexible enough to incorporate the knowledge of shop-floor workers on the production line.