Summary of Landmark-based Localization Using Stereo Vision and Deep Learning in Gps-denied Battlefield Environment, by Ganesh Sapkota and Sanjay Madria
Landmark-based Localization using Stereo Vision and Deep Learning in GPS-Denied Battlefield Environment
by Ganesh Sapkota, Sanjay Madria
First submitted to arxiv on: 19 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach to localization in non-GPS battlefield environments using only passive camera sensors and naturally occurring or artificial landmarks as anchors. The method utilizes a custom-calibrated stereo vision camera for distance estimation, YOLOv8s model for landmark recognition, and efficient stereomatching algorithm for depth image generation. The position of the unknown node is then calculated using the least square algorithm and optimized with the L-BFGS-B method. Experimental results show that the proposed framework outperforms existing anchor-based DV-Hop algorithms and competes with vision-based methods in terms of localization error (RMSE). This paper has implications for military operations, where accurate location information can be crucial. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us find our way in tough situations by using cameras instead of GPS. It’s like having a superpower that lets you know exactly where you are, even when the usual ways don’t work. The scientists used special cameras and computers to figure out how to do this, and it worked really well! They tested it against other methods and showed that their way is just as good or better. This could be very helpful for people in situations where they need to know exactly where they are. |
Keywords
* Artificial intelligence * Image generation
