| Budget Amount *help |
¥4,550,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥1,050,000)
Fiscal Year 2019: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2018: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2017: ¥2,730,000 (Direct Cost: ¥2,100,000、Indirect Cost: ¥630,000)
|
|---|
| Outline of Final Research Achievements |
The purpose of this research project is to realize robust SLAM using feature points which are automatically extracted by deep learning in order to perform highly accurate self-localization in a non-artificial environment represented by agriculture and forestry fields. The main research results are as follows: 1) To identify weeds and crops, we proposed a multi-modal deep learning system that utilizes temperature information in addition to RGB and Depth. 2) We proposed image processing algorithm to efficiently acquire a large amount of data used for deep learning. 3) In order to realize a system that can autonomously drive at low cost in the agriculture field, we constructed a system that recognizes the environment using an inexpensive Depth sensor and verified the effectiveness by simulation and actual machine experiments.
|
