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arXiv · 2609.14543

NavPatch: Evidence-Guided Object-Level Costmap Correction with Vision-Language Models

Shiji Sun·Xingyu Tao·Hao Wang·Ling Wang·Zhengyi Chen
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Abstract

Mobile robots typically rely on geometric maps for obstacle avoidance and path planning, but the resulting obstacle representation does not always match how an object should affect navigation. A low lying cable may be missed, a flexible curtain may create spurious blockage, and a traffic cone may require an exclusion region larger than its observed footprint. We present NavPatch, an object level correction layer that assigns ADD, REMOVE, or EXTEND to navigation relevant object categories through periodic scene understanding with a vision-language model. Open vocabulary grounding localizes object instances, and LiDAR and RGB-D observations provide 3D support. Observation quality filtering and cross frame maintenance determine when each correction patch is committed, replaced, or revoked. In 50 real robot trials across five layouts, NavPatch achieves an overall success rate of 86.0%. An ablation study of four configurations with 200 runs in total shows that NavPatch improves the success rate from 70.0% to 86.0% and reduces the false commit rate from 68.4% to 40.7% compared with updates based only on the current observation.

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Shiji Sun, Xingyu Tao, Hao Wang, Ling Wang, Zhengyi Chen. 2026-09-13. NavPatch: Evidence-Guided Object-Level Costmap Correction with Vision-Language Models. https://arxiv.org/abs/2609.14543

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