SemGeoNav:A Safety-Guided Visual Navigation Approach with Semantic Reasoning and Geometric Planning

• URL: http://arxiv.org/abs/2606.16400v1
• Date: Mon, 15 Jun 2026 08:38:46 GMT
• Title: SemGeoNav:A Safety-Guided Visual Navigation Approach with Semantic Reasoning and Geometric Planning
• Abstract summary: SemGeoNav is a novel hierarchical visual navigation framework.<n>It tightly integrates the high-level semantic reasoning of end-to-end models with the reliable local planning ability of geometry-based methods.<n>We evaluate SemGeoNav on a Unitree Go2 quadruped robot in real-world environments.
• Score: 8.096489080645487
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning-based visual navigation has enhanced semantic goal-reaching capabilities. However, due to their black-box nature, purely end-to-end models often lack explicit geometric constraints, leading to unpredictable and unreliable obstacle avoidance in open environments. Conversely, traditional geometric planners ensure safety but struggle with high-dimensional visual targets. To address these limitations, we propose SemGeoNav, a novel hierarchical visual navigation framework.It tightly integrates the high-level semantic reasoning of end-to-end models with the reliable local planning ability of geometry-based methods, achieving robust image-based navigation while significantly improving obstacle avoidance. Furthermore, we introduce a temporal trajectory smoothing mechanism to ensure continuous and stable robot motion. We evaluated SemGeoNav on a Unitree Go2 quadruped robot in real-world environments. The results demonstrate that SemGeoNav outperforms existing representative methods, including ViNT and NoMaD, achieving higher success rates and shorter navigation times.

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