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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.15643 |
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| _version_ | 1866910916375740416 |
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| author | Ieong, I-Tak Tang, Hao |
| author_facet | Ieong, I-Tak Tang, Hao |
| contents | Goal-oriented navigation presents a fundamental challenge for autonomous systems, requiring agents to navigate complex environments to reach designated targets. This survey offers a comprehensive analysis of multimodal navigation approaches through the unifying perspective of inference domains, exploring how agents perceive, reason about, and navigate environments using visual, linguistic, and acoustic information. Our key contributions include organizing navigation methods based on their primary environmental reasoning mechanisms across inference domains; systematically analyzing how shared computational foundations support seemingly disparate approaches across different navigation tasks; identifying recurring patterns and distinctive strengths across various navigation paradigms; and examining the integration challenges and opportunities of multimodal perception to enhance navigation capabilities. In addition, we review approximately 200 relevant articles to provide an in-depth understanding of the current landscape. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_15643 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multimodal Perception for Goal-oriented Navigation: A Survey Ieong, I-Tak Tang, Hao Robotics Goal-oriented navigation presents a fundamental challenge for autonomous systems, requiring agents to navigate complex environments to reach designated targets. This survey offers a comprehensive analysis of multimodal navigation approaches through the unifying perspective of inference domains, exploring how agents perceive, reason about, and navigate environments using visual, linguistic, and acoustic information. Our key contributions include organizing navigation methods based on their primary environmental reasoning mechanisms across inference domains; systematically analyzing how shared computational foundations support seemingly disparate approaches across different navigation tasks; identifying recurring patterns and distinctive strengths across various navigation paradigms; and examining the integration challenges and opportunities of multimodal perception to enhance navigation capabilities. In addition, we review approximately 200 relevant articles to provide an in-depth understanding of the current landscape. |
| title | Multimodal Perception for Goal-oriented Navigation: A Survey |
| topic | Robotics |
| url | https://arxiv.org/abs/2504.15643 |