aiqbench analysis
SemGeoNav addresses the critical trade-off between semantic understanding and geometric safety in autonomous navigation by hybridizing end-to-end learning models with traditional planning constraints. This approach enables more reliable obstacle avoidance for quadruped robots like the Unitree Go2, directly advancing practical applications of machine learning in complex physical environments. The integration ensures that AI-driven decisions remain predictable and safe without sacrificing high-level goal-reaching capabilities.
SemGeoNav:A Safety-Guided Visual Navigation Approach with Semantic Reasoning and Geometric Planning
arXiv:2606.16400v1 Announce Type: new 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.
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 this http URL 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.
Robotics (cs.RO)
arXiv:2606.16400 [cs.RO]
(or arXiv:2606.16400v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.16400 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 227 words