CNN-SVO: improving the mapping in semi-direct visual odometry using single-image depth prediction

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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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Bibliographic Details
Main Authors:	Loo, Shing Yan, Amiri, Ali Jahani, Mashohor, Syamsiah, Tang, Sai Hong, Zhang, Hong
Format:	Conference or Workshop Item
Language:	English
Published:	IEEE 2019
Online Access:	http://psasir.upm.edu.my/id/eprint/36196/ 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

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