Landmark-based Localization using Stereo Vision and Deep Learning in
  GPS-Denied Battlefield Environment
Ganesh Sapkota Sanjay Madria
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).