Sensor Fusion for Motion Estimation of Mobile Robots with Compensation for Out-of-Sequence Measurements
(2011) 2011 11th International Conference on Control, Automation and Systems p.211-216- Abstract
- The position and orientation estimation problem for mobile robots is approached by fusing measurements from inertial sensors, wheel encoders, and a camera. The sensor fusion approach is based on the standard extended Kalman filter, which is modified to handle measurements from the camera with unknown prior delay. A real-time implementation is done on a four-wheeled omni-directional mobile robot, using a dynamic model with 11 states. The algorithm is analyzed and validated with simulations and experiments.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/2277200
- author
- Berntorp, Karl LU ; Årzén, Karl-Erik LU and Robertsson, Anders LU
- organization
- publishing date
- 2011
- type
- Contribution to conference
- publication status
- published
- subject
- keywords
- mobile robotics, estimation, localization, Extended Kalman filter, out-of-sequence, sensor fusion
- pages
- 211 - 216
- conference name
- 2011 11th International Conference on Control, Automation and Systems
- conference dates
- 2011-10-26
- external identifiers
-
- wos:000300490000040
- scopus:84856555888
- project
- ENGROSS
- language
- English
- LU publication?
- yes
- additional info
- month=October key=bern_etal2011ccas
- id
- 8c506f63-0d4e-478d-ace9-03637fa0a75b (old id 2277200)
- date added to LUP
- 2016-04-04 14:35:27
- date last changed
- 2024-03-06 19:24:55
@misc{8c506f63-0d4e-478d-ace9-03637fa0a75b, abstract = {{The position and orientation estimation problem for mobile robots is approached by fusing measurements from inertial sensors, wheel encoders, and a camera. The sensor fusion approach is based on the standard extended Kalman filter, which is modified to handle measurements from the camera with unknown prior delay. A real-time implementation is done on a four-wheeled omni-directional mobile robot, using a dynamic model with 11 states. The algorithm is analyzed and validated with simulations and experiments.}}, author = {{Berntorp, Karl and Årzén, Karl-Erik and Robertsson, Anders}}, keywords = {{mobile robotics; estimation; localization; Extended Kalman filter; out-of-sequence; sensor fusion}}, language = {{eng}}, pages = {{211--216}}, title = {{Sensor Fusion for Motion Estimation of Mobile Robots with Compensation for Out-of-Sequence Measurements}}, url = {{https://lup.lub.lu.se/search/files/6395373/2277403.pdf}}, year = {{2011}}, }