An Approach to Reduce Computational Cost for Localization Problem
One of the biggest factors that contribute to the computational cost of extended Kalman filter-based SLAM is the covariance update. This is due to the multiplications of the covariance matrix with other parameters and the increment of its dimension, which is twice the number of landmarks. Therefore...
à®®à¯à®´à¯ விளகà¯à®•à®®à¯
Saved in:
| தலைமை எழà¯à®¤à¯à®¤à®¾à®³à®°à¯à®•à®³à¯: | , |
|---|---|
| வடிவமà¯: | Conference or Workshop Item |
| வெளியீடபà¯à®ªà®Ÿà¯à®Ÿà®¤à¯: |
2014
|
| பகà¯à®¤à®¿à®•à®³à¯: | |
| நிகழà¯à®¨à®¿à®²à¯ˆ அணà¯à®•à®²à¯: | http://umpir.ump.edu.my/9786/ http://umpir.ump.edu.my/9786/1/An%20Approach%20to%20Reduce%20Computational%20Cost%20for%20Localization%20Problem.pdf http://umpir.ump.edu.my/9786/7/An%20Approach%20to%20Reduce%20Computational%20Cost%20for%20Localization%20Problem%20-%20Abstract.pdf |
| கà¯à®±à®¿à®¯à¯€à®Ÿà¯à®•à®³à¯: |
கà¯à®±à®¿à®šà¯à®šà¯Šà®²à¯ இணை
கà¯à®±à®¿à®¯à¯€à®Ÿà¯à®•à®³à¯ இலà¯à®²à¯ˆ, இநà¯à®¤ கà¯à®±à®¿à®šà¯à®šà¯Šà®²à¯à®²à¯ˆ à®®à¯à®¤à®²à®¿à®²à¯ பதிவ௠செயà¯à®¯à¯à®™à¯à®•à®³à¯!
|