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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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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