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UKF-SLAM Based Gravity Gradient Aided Navigation

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UKF-SLAM Based Gravity Gradient Aided Navigation. / Weng, Y.; Wu, M.a.
Intelligent Robotics and Applications: 7th International Conference, ICIRA 2014, Guangzhou, China, December 17-20, 2014, Proceedings, Part I. Springer, 2014. p. 77-88.

Research output: Chapter in Book/Report/Conference proceeding › Chapter

HarvardHarvard

Weng, Y & Wu, MA 2014, UKF-SLAM Based Gravity Gradient Aided Navigation. in Intelligent Robotics and Applications: 7th International Conference, ICIRA 2014, Guangzhou, China, December 17-20, 2014, Proceedings, Part I. Springer, pp. 77-88.

APA

Weng, Y., & Wu, M. A. (2014). UKF-SLAM Based Gravity Gradient Aided Navigation. In Intelligent Robotics and Applications: 7th International Conference, ICIRA 2014, Guangzhou, China, December 17-20, 2014, Proceedings, Part I (pp. 77-88). Springer.

CBE

Weng Y, Wu MA. 2014. UKF-SLAM Based Gravity Gradient Aided Navigation. In Intelligent Robotics and Applications: 7th International Conference, ICIRA 2014, Guangzhou, China, December 17-20, 2014, Proceedings, Part I. Springer. pp. 77-88.

MLA

Weng, Y. and M.a. Wu "UKF-SLAM Based Gravity Gradient Aided Navigation". Intelligent Robotics and Applications: 7th International Conference, ICIRA 2014, Guangzhou, China, December 17-20, 2014, Proceedings, Part I. Springer. 2014, 77-88.

VancouverVancouver

Weng Y, Wu MA. UKF-SLAM Based Gravity Gradient Aided Navigation. In Intelligent Robotics and Applications: 7th International Conference, ICIRA 2014, Guangzhou, China, December 17-20, 2014, Proceedings, Part I. Springer. 2014. p. 77-88

Author

Weng, Y. ; Wu, M.a. / UKF-SLAM Based Gravity Gradient Aided Navigation. Intelligent Robotics and Applications: 7th International Conference, ICIRA 2014, Guangzhou, China, December 17-20, 2014, Proceedings, Part I. Springer, 2014. pp. 77-88

RIS

TY - CHAP

T1 - UKF-SLAM Based Gravity Gradient Aided Navigation

AU - Weng, Y.

AU - Wu, M.a.

PY - 2014/12/3

Y1 - 2014/12/3

N2 - Considering the two characteristics: (1) simultaneous localization and mapping (SLAM) is a popular algorithm for autonomous underwater robot, but visual SLAM is significantly influenced by weak illumination; (2) geomagnetism-aided navigation and gravity-aided navigation are equally important methods in the field of robot navigation, but both are affected heavily by time-varying noises and terrain fluctuations; however, gravity gradient vector can avoid the influence of time-varying noises, and is less affected by terrain fluctuations. To the end, we proposes a UKF-SLAM based gravity gradient aided navigation in this paper with the following advantages: (1) the UKF-SLAM is an efficient way to avoid linearization errors compared with the EKF-SLAM; (2) it improves the accuracy of navigation system without the help of any geophysical reference map; (3) it is suitable for a robot to navigate itself under the environment of weak illumination and time-varying disturbances. Experimental results also show that our proposed method has a less localization error than the SLAM-based geomagnetic aided navigation.

AB - Considering the two characteristics: (1) simultaneous localization and mapping (SLAM) is a popular algorithm for autonomous underwater robot, but visual SLAM is significantly influenced by weak illumination; (2) geomagnetism-aided navigation and gravity-aided navigation are equally important methods in the field of robot navigation, but both are affected heavily by time-varying noises and terrain fluctuations; however, gravity gradient vector can avoid the influence of time-varying noises, and is less affected by terrain fluctuations. To the end, we proposes a UKF-SLAM based gravity gradient aided navigation in this paper with the following advantages: (1) the UKF-SLAM is an efficient way to avoid linearization errors compared with the EKF-SLAM; (2) it improves the accuracy of navigation system without the help of any geophysical reference map; (3) it is suitable for a robot to navigate itself under the environment of weak illumination and time-varying disturbances. Experimental results also show that our proposed method has a less localization error than the SLAM-based geomagnetic aided navigation.

UR - http://link.springer.com/chapter/10.1007/978-3-319-13966-1_8#page-1

M3 - Chapter

SN - 9783319139654

SP - 77

EP - 88

BT - Intelligent Robotics and Applications: 7th International Conference, ICIRA 2014, Guangzhou, China, December 17-20, 2014, Proceedings, Part I

PB - Springer

ER -