vijay4313/SLAM-using-GTSAM

This project is a classic example of a legacy academic or personal experiment, evidenced by its age (over 8 years) and zero recent activity. While it uses GTSAM— a powerful and industrially relevant factor graph optimization library—the project itself provides no unique moat or novel optimization. With only 18 stars and 11 forks, it lacks the community momentum required to compete with modern SLAM frameworks like ORB-SLAM3, Kimera, or LIO-SAM. From a frontier perspective, companies like NVIDIA (via Isaac ROS), Skydio, and various drone platforms have already integrated far more robust, hardware-accelerated SLAM pipelines. The defensibility is low because the project serves primarily as a learning resource or a basic proof-of-concept rather than a production-ready tool. It is effectively displaced by both newer open-source standards and proprietary spatial AI stacks.