Landmark-based Localization using Stereo Vision and Deep Learning in GPS-Denied Battlefield Environment
<|reference_start|>Landmark-based Localization using Stereo Vision and Deep Learning in GPS-Denied Battlefield Environment: Localization in a battlefield environment is increasingly challenging as GPS connectivity is often denied or unreliable, and physical deployment of anchor nodes across wireless networks for localization can be difficult in hostile battlefield terrain. Existing range-free localization methods rely on radio-based anchors and their average hop distance which suffers from accuracy and stability in dynamic and sparse wireless network topology. Vision-based methods like SLAM and Visual Odometry use expensive sensor fusion techniques for map generation and pose estimation. This paper proposes a novel framework for localization in non-GPS battlefield environments using only the passive camera sensors and considering naturally existing or artificial landmarks as anchors. The proposed method utilizes a customcalibrated stereo vision camera for distance estimation and the YOLOv8s model, which is trained and fine-tuned with our real-world dataset for landmark recognition. The depth images are generated using an efficient stereomatching algorithm, and distances to landmarks are determined by extracting the landmark depth feature utilizing a bounding box predicted by the landmark recognition model. The position of the unknown node is then obtained using the efficient least square algorithm and then optimized using the L-BFGS-B (limited-memory quasi-Newton code for bound-constrained optimization) method. Experimental results demonstrate that our proposed framework performs better than existing anchorbased DV-Hop algorithms and competes with the most efficient vision-based algorithms in terms of localization error (RMSE).<|reference_end|>
@article{sapkota2024landmark-based,
    title={Landmark-based Localization using Stereo Vision and Deep Learning in
  GPS-Denied Battlefield Environment},
    author={Ganesh Sapkota and Sanjay Madria},
    journal={arXiv preprint arXiv:2402.12551},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.12551},
    primaryClass={cs.CV cs.AI}
}