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.
To realize the self-localization of autonomous robots, methods for the 2D motion measurement of robots are required. In this research, a self-localization system using a CCD camera is proposed. In the proposed system, the self-localization is estimated by movement tracking using some keypoints detected from the floor images captured by the CCD camera. For the illumination of floor image, two LED illuminate are used. These lighting systems are installed in such a way that lit from both sides of the floor and parallel to the floor so that some minimal bumps or original veins are captured from the floor. An accelerated KAZE feature is applied in the proposed system for keypoint detection and for computing resulting in the generation of the descriptor. In the estimation, the location information from the previous step and the estimated position one frame ahead are used. Using the estimated result and computed descriptor, the proposed system matches the nearest keypoint between the estimated keypoint detected from the previous floor image and the keypoint detected from the next floor image at the beginning. The Hamming distance is employed in the proposed system to evaluate the matching. If the Hamming distance is longer than a threshold, the proposed system tries to match from the 2nd nearest keypoint. Based on an experiment in which the measurement distance and computation time are investigated, the effectiveness of the proposed method is confirmed. INDEX TERMS Visual odometry, vision measurement, image motion analysis, odometry measurement, AKAZE features, image matching.
Elderly people are at the risk of serious injuries by falling. As most of falls result from tripping, researchers assess the ease of tripping using the minimum toe clearance (MinTC) measure, which is known to be more variable in the elderly than in younger people. This paper proposes a method that judges the ease of tripping from the MinTC height measured by a One Dimensional Brightness Distribution Sensor (Obrid-Sensor). Using our method, we detected the difference between normal and dangerous gaits, and found significant differences between two gait types. In conclusion, our proposed system clearly distinguished normal from dangerous gaits. In future work, the method will be further adapted to actual environments by widening its detection area.
Falls of elderly during walking cause serious injury as fracture probably. Most of falls results from tripping, Minimum Toe Clearance(MinTC) indicating easiness to tripping was studied by a lot of researchers. It was reported that variability of MinTC was greater in the elderly than the young. This paper proposes a method to make a judgement on easiness to tripping based on the height of MinTC using One Dimensional Brightness Distribution Sensor. For the purpose, we detected the difference between normal gait and dangerous gait using our method. As a result, our method obtained the significant difference between normal and dangerous gait. In conclusion, we clearly judged normal gait or dangerous gait using our proposal system. In the future, we should test at the actual environment, develop further.
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