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For the estimation of self-position in an autonomous robot, there is a requirement to measure the two-dimensional movement. Therefore, a self-position estimation method using the floor image photographed with a CCD camera is proposed. In this paper, the self-position is estimated by detected keypoints from the image photographed before and after movement. AKAZE is used for the extraction of the features. Using the extracted existing frame by the past movement prediction, the movement position of 1 frame ahead of each keypoints can be predicted. Therefore, using the movement prediction of the feature point, in comparison with feature matching method, a method to speed up processing is proposed.

The number of accidents involving elderly individuals has been increasing with the increase of the aging population, posing increasingly serious challenges. Most accidents are caused by reduced judgment and physical abilities, which lead to severe consequences. Therefore, studies on support systems for elderly and visually impaired people to improve the safety and quality of daily life are attracting considerable attention. In this study, a road surface condition distinction method using reflection intensities obtained by an ultrasonic sensor was proposed. The proposed method was applied to movement support systems for elderly and visually impaired individuals to detect dangerous road surfaces and give an alarm. The method did not perform well in previous studies of puddle detection, because the alert provided by the method did not enable users to avoid puddles. This study extended the method proposed by previous studies with respect to puddle detection ability. The findings indicate the effectiveness of the proposed method by considering four road surface conditions. The proposed method could detect puddle conditions. The effectiveness of the proposed method was verified in all four conditions, since users could differentiate between road surface conditions and classify the conditions as either safe or dangerous.