Zobrazeno 1 - 10
of 285
pro vyhledávání: '"Yoji KURODA"'
Publikováno v:
Nihon Kikai Gakkai ronbunshu, Vol 89, Iss 923, Pp 23-00034-23-00034 (2023)
In this paper, we propose a spatial measurement method based on object geometry and velocity information using past measurement information from 3D-LiDAR. If geometric information is obtained from 3D-LiDAR, it can be applied to global localization, o
Externí odkaz:
https://doaj.org/article/93d6c574076f4aee990ade98c91ab769
Publikováno v:
ROBOMECH Journal, Vol 8, Iss 1, Pp 1-12 (2021)
Abstract This paper presents an EKF (extended Kalman filter) based self-attitude estimation method with a LiDAR DNN (deep neural network) learning landscape regularities. The proposed DNN infers the gravity direction from LiDAR data. The point cloud
Externí odkaz:
https://doaj.org/article/64ceecbb080d4321a00310db2e5a4e00
Publikováno v:
International Journal of Advanced Robotic Systems, Vol 19 (2022)
This article proposes a mutual positioning relay method that enables multiple robots to monitor indoor environments. Here, robots refer to a small number of parent robots with high positioning performance and a large number of child robots with minim
Externí odkaz:
https://doaj.org/article/4cc5d1d28f9646b3ba1e7c9d5924b070
Autor:
Ryota Ozaki, Yoji Kuroda
Publikováno v:
ROBOMECH Journal, Vol 8, Iss 1, Pp 1-12 (2021)
Abstract This paper presents an EKF-based self-attitude estimation with a DNN (deep neural network) learning landscape information. The method integrates gyroscopic angular velocity and DNN inference in the EKF. The DNN predicts a gravity vector in a
Externí odkaz:
https://doaj.org/article/59ca0ec9476c4694b2c65f988ab61b84
Autor:
Ryota OZAKI, Yoji KURODA
Publikováno v:
Nihon Kikai Gakkai ronbunshu, Vol 87, Iss 903, Pp 21-00098-21-00098 (2021)
This paper presents a real-time self-attitude estimation method which utilizes the clues to the direction of the gravity hidden in images and structures of the environments. In the proposed method, the angular velocity is integrated using a gyroscope
Externí odkaz:
https://doaj.org/article/570bc29e62624456bcf7231475302d5d
Publikováno v:
Nihon Kikai Gakkai ronbunshu, Vol 87, Iss 899, Pp 21-00125-21-00125 (2021)
In this paper, we propose a pedestrian trajectory prediction method for autonomous mobile robots. In many cases, there are many pedestrians in the environment in which the autonomous mobile robot runs. In such an environment, the robot needs to run s
Externí odkaz:
https://doaj.org/article/88e9041acb624ae7acb491a7293cb1d9
Autor:
Kazuki TAKAHASHI, Yoji KURODA
Publikováno v:
Nihon Kikai Gakkai ronbunshu, Vol 86, Iss 892, Pp 20-00102-20-00102 (2020)
When a mobile robot moves in an environment where there are moving obstacles such as pedestrians and other robots, the robot is required to avoid collisions with these obstacles. However, trying to avoid all the predicted positions on the trajectory
Externí odkaz:
https://doaj.org/article/caeb8f83b82c441cb0127027ab5f914f
Autor:
Ryota OZAKI, Yoji KURODA
Publikováno v:
Nihon Kikai Gakkai ronbunshu, Vol 85, Iss 875, Pp 19-00065-19-00065 (2019)
This paper presents a real-time 6DoF localization method which corrects accumulative error by estimating relative poses to building walls for mobile robots in urban areas. This method exploits a fact that most of all artificial walls are built vertic
Externí odkaz:
https://doaj.org/article/140b1ad16a314516b9bbca6caa1da692
Publikováno v:
Nihon Kikai Gakkai ronbunshu, Vol 85, Iss 875, Pp 19-00064-19-00064 (2019)
In the conventional intersection recognition method, shape information is used. In order to recognize an intersection composed of roads divided by grass and asphalt, it is necessary to distinguish them. There is almost no difference in shape between
Externí odkaz:
https://doaj.org/article/73f1df01e3ea433886dad29cf48fa98b
Autor:
Hibiki Kawai, Yoji Kuroda
Publikováno v:
2023 IEEE/SICE International Symposium on System Integration (SII).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::153a2023ded25dad668b6193e5508565
https://doi.org/10.1109/sii55687.2023.10039292
https://doi.org/10.1109/sii55687.2023.10039292