DIV-Nav: 多物体导航中的开放词汇空间关系
DIV-Nav: Open-Vocabulary Spatial Relationships for Multi-Object Navigation
AI总结：
本文提出DIV-Nav系统，通过分解复杂空间约束为简单物体查询、计算语义信念图交集以及利用LVLM验证，实现基于开放词汇空间关系的多物体导航。
摘要：
开放词汇语义映射和物体导航的进步使机器人能够对任意物体进行有信息的环境搜索。然而，此类零样本物体导航通常针对简单查询，如
英文摘要：
Advances in open-vocabulary semantic mapping and object navigation have enabled robots to perform an informed search of their environment for an arbitrary object. However, such zero-shot object navigation is typically designed for simple queries with an object name like "television" or "blue rug". Here, we consider more complex free-text queries with spatial relationships, such as "find the remote on the table" while still leveraging robustness of a semantic map. We present DIV-Nav, a real-time navigation system that efficiently addresses this problem through a series of relaxations: i) Decomposing natural language instructions with complex spatial constraints into simpler object-level queries on a semantic map, ii) computing the Intersection of individual semantic belief maps to identify regions where all objects co-exist, and iii) Validating the discovered objects against the original, complex spatial constrains via a LVLM. We further investigate how to adapt the frontier exploration objectives of online semantic mapping to such spatial search queries to more effectively guide the search process. We validate our system through extensive experiments on the MultiON benchmark and real-world deployment on a Boston Dynamics Spot robot using a Jetson Orin AGX. More details and videos are available at https://anonsub42.github.io/reponame/
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
