Multi-Source Temporal-Depth fusion for robust end-to-End visual odometry.
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| Title: | Multi-Source Temporal-Depth fusion for robust end-to-End visual odometry. |
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| Authors: | Zhang S; School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi'an, PR China; School of Computer Science, Northwestern Polytechnical University, Xi'an, PR China., Cao C; School of Computer Science, Northwestern Polytechnical University, Xi'an, PR China., Gao Q; General Department IV, Xi'an, Institute of Applied Optics, Xi'an, PR China., Liu G; School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi'an, PR China. Electronic address: liuganchao@nwpu.edu.cn. |
| Source: | Neural networks : the official journal of the International Neural Network Society [Neural Netw] 2026 Jun; Vol. 198, pp. 108598. Date of Electronic Publication: 2026 Jan 17. |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Pergamon Press Country of Publication: United States NLM ID: 8805018 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-2782 (Electronic) Linking ISSN: 08936080 NLM ISO Abbreviation: Neural Netw Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
| ISSN: | 1879-2782 |
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| DOI: | 10.1016/j.neunet.2026.108598 |
