Part SLAM: A Succinct and Discriminative Method for Scene Matching via Unsupervised Part-based Modeling
| Project/Area Number |
26330297
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Intelligent robotics
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| Research Institution | University of Fukui |
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Principal Investigator |
Tanaka Kanji 福井大学, 学術研究院工学系部門, 准教授 (30325899)
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| Project Period (FY) |
2014-04-01 – 2018-03-31
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| Project Status |
Completed (Fiscal Year 2017)
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| Budget Amount *help |
¥4,810,000 (Direct Cost: ¥3,700,000、Indirect Cost: ¥1,110,000)
Fiscal Year 2016: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2015: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2014: ¥2,470,000 (Direct Cost: ¥1,900,000、Indirect Cost: ¥570,000)
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| Keywords | 視覚移動ロボット / 自己位置推定 / 地図作成 / 情景部品モデル / 深層学習 / 変化検出 / 移動ロボット / SLAM / 部品モデル / 深層畳込みニューラルネットワーク / 物体認識 / データマイニング / ロボットビジョン / 地図生成 / ＳＬＡＭ / 部品SLAM / 共通物体発見 |
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| Outline of Final Research Achievements |
In this study, we realized a next generation SLAM (online map learning) technique named ``part SLAM". More formally, we are based on a light-weight and high-accuracy compressive map representation ``unsupervised scene part model" and developed the new SLAM technique. Furthermore, we developed a versatile SLAM system based on monocular camera and verified efficacy of the developed system in a challenging problem called ``cross-season visual place recognition".
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Report
(5 results)
Research Products
(27 results)
