Landmark Stereo Dataset for Landmark Recognition and Moving Node
  Localization in a Non-GPS Battlefield Environment
Ganesh Sapkota Sanjay Madria
In this paper, we have proposed a new strategy of using the landmark anchor
node instead of a radio-based anchor node to obtain the virtual coordinates
(landmarkID, DISTANCE) of moving troops or defense forces that will help in
tracking and maneuvering the troops along a safe path within a GPS-denied
battlefield environment. The proposed strategy implements landmark recognition
using the Yolov5 model and landmark distance estimation using an efficient
Stereo Matching Algorithm. We consider that a moving node carrying a low-power
mobile device facilitated with a calibrated stereo vision camera that captures
stereo images of a scene containing landmarks within the battlefield region
whose locations are stored in an offline server residing within the device
itself. We created a custom landmark image dataset called MSTLandmarkv1 with 34
landmark classes and another landmark stereo dataset of those 34 landmark
instances called MSTLandmarkStereov1. We trained the YOLOv5 model with
MSTLandmarkv1 dataset and achieved 0.95 mAP @ 0.5 IoU and 0.767 mAP @ [0.5:
0.95] IoU. We calculated the distance from a node to the landmark utilizing the
bounding box coordinates and the depth map generated by the improved SGM
algorithm using MSTLandmarkStereov1. The tuple of landmark IDs obtained from
the detection result and the distances calculated by the SGM algorithm are
stored as the virtual coordinates of a node. In future work, we will use these
virtual coordinates to obtain the location of a node using an efficient
trilateration algorithm and optimize the node position using the appropriate
optimization method.