Beyond Handcrafted Features: A Deep Learning Framework for Optical Flow and SLAM
This paper presents a novel approach for visual Simultaneous Localization and Mapping (SLAM) using Convolution Neural Networks (CNNs) for robust map creation. Traditional SLAM methods rely on handcrafted features, which are susceptible to viewpoint changes, occlusions, and illumination variations. T...
Saved in:
| Main Authors: | Kamran Kazi, Arbab Nighat Kalhoro, Farida Memon, Azam Rafique Memon, Muddesar Iqbal |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
|
| Series: | Journal of Imaging |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2313-433X/11/5/155 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Robust outdoor trajectory mapping using CNN features and loop closure optimization
by: Kamran Kazi, et al.
Published: (2025-07-01) -
Um poetry slam indígena: a poesia falada no Slam Coalkan
by: Fabiana Oliveira de Souza
Published: (2023-01-01) -
UDGS-SLAM: UniDepth Assisted Gaussian Splatting for Monocular SLAM
by: Mostafa Mansour, et al.
Published: (2025-07-01) -
YOLO-NeRFSLAM: underwater object detection for the visual NeRF-SLAM
by: Zhe Wang, et al.
Published: (2025-06-01) -
Monocular Object-Level SLAM Enhanced by Joint Semantic Segmentation and Depth Estimation
by: Ruicheng Gao, et al.
Published: (2025-03-01)