FOM-Nav: Frontier-Object Maps for Object Goal Navigation

• URL: http://arxiv.org/abs/2512.01009v1
• Date: Sun, 30 Nov 2025 18:16:09 GMT
• Title: FOM-Nav: Frontier-Object Maps for Object Goal Navigation
• Authors: Thomas Chabal, Shizhe Chen, Jean Ponce, Cordelia Schmid,
• Abstract summary: FOM-Nav is a framework that enhances exploration efficiency through Frontier-Object Maps and vision-language models.<n>To train FOM-Nav, we automatically construct large-scale navigation datasets from real-world scanned environments.<n> FOM-Nav achieves state-of-the-art performance on the MP3D and HM3D benchmarks, particularly in navigation efficiency metric SPL.
• Score: 65.76906445210112
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper addresses the Object Goal Navigation problem, where a robot must efficiently find a target object in an unknown environment. Existing implicit memory-based methods struggle with long-term memory retention and planning, while explicit map-based approaches lack rich semantic information. To address these challenges, we propose FOM-Nav, a modular framework that enhances exploration efficiency through Frontier-Object Maps and vision-language models. Our Frontier-Object Maps are built online and jointly encode spatial frontiers and fine-grained object information. Using this representation, a vision-language model performs multimodal scene understanding and high-level goal prediction, which is executed by a low-level planner for efficient trajectory generation. To train FOM-Nav, we automatically construct large-scale navigation datasets from real-world scanned environments. Extensive experiments validate the effectiveness of our model design and constructed dataset. FOM-Nav achieves state-of-the-art performance on the MP3D and HM3D benchmarks, particularly in navigation efficiency metric SPL, and yields promising results on a real robot.

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