| Project/Area Number |
10650440
|
|---|
| Research Category |
Grant-in-Aid for Scientific Research (C)
|
| Allocation Type | Single-year Grants |
|---|
| Section | 一般 |
|---|
| Research Field |
Control engineering
|
|---|
| Research Institution | Shizuoka Institute of Science and Technology |
|---|
Principal Investigator |
NIWA Shohei Shizuoka Institute of Science and Technology, Professor, 理工学部, 教授 (30023287)
|
|---|
| Co-Investigator(Kenkyū-buntansha) |
ANDO Yoshinori Nagoya University, Assistant Professor, 工学研究科, 講師 (70242831)
SUZUKI Masayuki Nagoya University, Professor, 工学研究科, 教授 (20023286)
|
|---|
| Project Period (FY) |
1998 – 1999
|
| Project Status |
Completed (Fiscal Year 2001)
|
| Budget Amount *help |
¥2,600,000 (Direct Cost: ¥2,600,000)
Fiscal Year 1999: ¥900,000 (Direct Cost: ¥900,000)
Fiscal Year 1998: ¥1,700,000 (Direct Cost: ¥1,700,000)
|
|---|
| Keywords | Sensor fusion / Kalman filter / Mobile robot / Image processing / Visual sensor / Inertial sensor |
|---|
| Research Abstract |
In this research, a development of the sensor fusion algorithm for control of a mobile vehicle is investigated. The output data from the visual sensor include a time-lag due to the image processing computation. The sampling rate of the visual sensor is considerably low so that it should be used with other sensors to control fast motion. The development of an algorithm to integrate the data from multi-sensor system to obtain the optimal estimate of the state variables is an important problem for control and navigation of mobile vehicle. The main purpose of this research is to develop a method which constitutes a sensor fusion system to give the optimal state estimates for control and navigation of mobile vehicle. The proposed sensor fusion system combines a vision sensor and a inertial sensor using a modified Kalman filter. A kind of multi-rate Kalman filter which treats the slow sampling rate data from the vision sensor is applied for the construction of the sensor fusion system. An experimental mobile robot is developed which realizes a sensor fusion system combining a vision sensor system and an inertial sensor system. Experimental results show that the proposed sensor fusion algorithm gives the precise estimates of the state variables combining the data from CCD camera images and the inertial sensors.
|
