2026-09-19T02:40:40Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1402022022-02-08T03:05:23Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Linares, Richard
Wenberg, Dakota
Massachusetts Institute of Technology. Department of Mechanical Engineering
2022-02-07T15:30:05Z
2022-02-07T15:30:05Z
2021-09
2021-09-30T17:31:40.262Z
https://hdl.handle.net/1721.1/140202
The Massachusetts Institute of Technology’s Lincoln Laboratory is developing a Lidar scanner to be used on a notional lander mission to Europa, a moon of Jupiter. The goal of this mission is to land safely on the unexplored rough terrain of the moon and analyze samples to detect the possibility of life in the subsurface oceans. A critical component to a safe landing is the ability to accurately estimate the state of the lander as it descends. This paper proposes the application of sensor fusion system that combines Inertial Measurement Unit measurements with relative pose data extracted from Lidar scans using an Extended Kalman Filter. The results show that the proposed system can accurately estimate the state in the Z and X axes while further improvements are required to increase the accuracy of the Y axis and the orientation.
S.M.
Massachusetts Institute of Technology
In Copyright - Educational Use Permitted
Copyright MIT
http://rightsstatements.org/page/InC-EDU/1.0/
Method for Kalman Filtering Pose Estimates from Lidar Scans During the Landing Phase
Thesis
application/pdf
Master
Master of Science in Mechanical Engineering
Publication
unknown
unknown
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<Abstract>The Massachusetts Institute of Technology’s Lincoln Laboratory is developing a Lidar scanner to be used on a notional lander mission to Europa, a moon of Jupiter. The goal of this mission is to land safely on the unexplored rough terrain of the moon and analyze samples to detect the possibility of life in the subsurface oceans. A critical component to a safe landing is the ability to accurately estimate the state of the lander as it descends. This paper proposes the application of sensor fusion system that combines Inertial Measurement Unit measurements with relative pose data extracted from Lidar scans using an Extended Kalman Filter. The results show that the proposed system can accurately estimate the state in the Z and X axes while further improvements are required to increase the accuracy of the Y axis and the orientation.</Abstract>
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