CNN-SVO: improving the mapping in semi-direct visual odometry using single-image depth prediction
Reliable feature correspondence between frames is a critical step in visual odometry (VO) and visual simultaneous localization and mapping (V-SLAM) algorithms. In comparison with existing VO and V-SLAM algorithms, semi-direct visual odometry (SVO) has two main advantages that lead to state-of-the-ar...
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| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
IEEE
2019
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| Online Access: | http://psasir.upm.edu.my/id/eprint/36196/1/CNN-SVO%20improving%20the%20mapping%20in%20semi-direct%20visual%20odometry%20using%20single-image%20depth%20prediction.pdf http://psasir.upm.edu.my/id/eprint/36196/ |
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