Large scale scenes such as multifloor homes can be robustly and efficiently mapped with a 3D graph of landmarks estimated jointly with robot poses in a factor graph, a technique commonly used in commercial robots such as drones and robot vacuums. In this work, we propose Language-Inferred Factor Graph for Instruction Following (LIFGIF), a zero-shot method to ground natural language instructions in such a map. LIFGIF also includes a policy for following natural language navigation instructions in a novel environment while the map is constructed, enabling robust navigation performance in the physical world. To evaluate LIFGIF, we present a new dataset, Object-Centric VLN (OC-VLN), in order to evaluate grounding of object-centric natural language navigation instructions. We compare to two state-of-the-art zero-shot baselines from related tasks, Object Goal Navigation and Vision Language Navigation, to demonstrate that LIFGIF outperforms them across all our evaluation metrics on OCVLN. Finally, we successfully demonstrate the effectiveness of LIFGIF for performing zero-shot object-centric instruction following in the real world on a Boston Dynamics Spot robot.