著者

友納 正裕 原 祥尭

出版者

一般社団法人 システム制御情報学会

雑誌

システム/制御/情報 (ISSN:09161600)

巻号頁・発行日

vol.64, no.2, pp.45-50, 2020-02-15 (Released:2020-08-15)

参考文献数

35

被引用文献数

2

著者

原 祥尭 坪内 孝司 大島 章

出版者

一般社団法人 日本機械学会

雑誌

日本機械学会論文集 (ISSN:21879761)

巻号頁・発行日

vol.82, no.834, pp.15-00421, 2016 (Released:2016-02-25)

参考文献数

14

被引用文献数

1 1

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We propose a new Grid-based SLAM method with partial map matching for considering previous sensor data in Rao-Blackwellized Particle Filter. The partial maps are built probabilistically as accumulated scan shapes for each particle. In conventional Grid-based SLAM methods, Rao-Blackwellized Particle Filter is often used. However, the conventional methods sometimes fall into misalignment and fail to build proper maps in large or limited visibility environments. In those environments, the sensor data becomes insufficient shape to match with maps for localization since the sensor field of view is limited. It causes misalignment and failure in localization and map building. Rao-Blackwellized Particle Filter of the conventional methods is based on a Hidden Markov Model that uses only current sensor data to estimate robot poses. Hence, if the current sensor data is insufficient, it is difficult to estimate robot poses and build maps correctly. In our new method, the Hidden Markov Model has been extended to utilize a series of sensor data from the past in several seconds to the present. The series of sensor data is accumulated to make scan shape sufficient for the matching in localization. Thus, the proposed method is expected to cover the lack of sensor field of view by means of the accumulation of sensor data, and is capable of mapping in large or limited visibility environments. In our experiments at Tsukuba Challenge 2014 and Tsudanuma Campus, consistent maps were built only by the proposed method. Shape errors of the maps built by the proposed method were smaller than the conventional method.

著者

熊田 大輔 原 祥尭 坪内 孝司 皿田 滋

出版者

一般社団法人 日本機械学会

雑誌

ロボティクス・メカトロニクス講演会講演概要集

巻号頁・発行日

vol.2015, pp._2A1-V08_1-_2A1-V08_4, 2015

被引用文献数

1

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This paper proposes road recognition using a laser scanner. In this study, road attributes are recognized by using a laser scanner is mounted on the robot. Road attributes are recognized using road surface shape and received light intensity that can be obtained by measuring a laser scanner towards the road surface. To recognize several road attributes using the two types of measurement data, feature values are calculated. And each road attribute is modeled by Gaussian distribution using the feature values within a certain range. Recognition the road attributes are based on the Mahalanobis distance between each model and the feature vector calculated from the measurement data. Using this method, We experimented at outdoor.

著者

原 祥尭 坪内 孝司 油田 信一

出版者

一般社団法人 日本機械学会

雑誌

ロボティクス・メカトロニクス講演会講演概要集

巻号頁・発行日

vol.2012, pp._2A1-L09_1-_2A1-L09_4, 2012

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We developed an acquisition method of maps and paths using ROS stacks for autonomous navigation by teaching playback framework. There are many strategies for autonomous navigation. In teaching playback framework, it is not a problem that maps are distorted continuously. However, if the maps are accurate, it is useful for human interfaces and extending the autonomous navigation area. Recently, the ROS platform has been used actively around the world. In ROS stacks, there is slam_gmapping stack that provides Grid-based SLAM using Rao-Blackwellized Particle Filter. Therefore we are trying to use ROS stacks for acquisition of maps and paths, and to evaluate its effectiveness for autonomous navigation.

著者

原 祥尭

出版者

一般社団法人 日本ロボット学会

雑誌

日本ロボット学会誌 (ISSN:02891824)

巻号頁・発行日

vol.35, no.4, pp.286-290, 2017 (Released:2017-06-15)

参考文献数

24

被引用文献数

1 3