In this paper, we introduce Semantic Layering in Room Segmentation via LLMs
(SeLRoS), an advanced method for semantic room segmentation by integrating
Large Language Models (LLMs) with traditional 2D map-based segmentation. Unlike
previous approaches that solely focus on the geometric segmentation of indoor
environments, our work enriches segmented maps with semantic data, including
object identification and spatial relationships, to enhance robotic navigation.
By leveraging LLMs, we provide a novel framework that interprets and organizes
complex information about each segmented area, thereby improving the accuracy
and contextual relevance of room segmentation. Furthermore, SeLRoS overcomes
the limitations of existing algorithms by using a semantic evaluation method to
accurately distinguish true room divisions from those erroneously generated by
furniture and segmentation inaccuracies. The effectiveness of SeLRoS is
verified through its application across 30 different 3D environments. Source
code and experiment videos for this work are available at:
this https URL

通过将大型语言模型（LLMs）与传统的二维地图分割相结合，我们引入了语义分层（Semantic Layering）对于房间分割的先进方法（SeLRoS），丰富了分割地图的语义数据，包括物体识别和空间关系，以提高机器人导航的准确性和上下文相关性，并使用语义评估方法准确区分真实房间分隔和由家具和分割不准确性错误生成的房间分隔。

基于 LLMs 的房间分割中的语义分层

Semantic Layering in Room Segmentation via LLMs

Object grounding tasks aim to locate the target object in an image through
verbal communications. Understanding human command is an important process
needed for effective human-robot communication. However, this is challenging
because human commands can be ambiguous and erroneous. This paper aims to
disambiguate the human's referring expressions by allowing the agent to ask
relevant questions based on semantic data obtained from scene graphs. We test
if our agent can use relations between objects from a scene graph to ask
semantically relevant questions that can disambiguate the original user
command. In this paper, we present Incremental Grounding using Scene Graphs
(IGSG), a disambiguation model that uses semantic data from an image scene
graph and linguistic structures from a language scene graph to ground objects
based on human command. Compared to the baseline, IGSG shows promising results
in complex real-world scenes where there are multiple identical target objects.
IGSG can effectively disambiguate ambiguous or wrong referring expressions by
asking disambiguating questions back to the user.

本文介绍了一种基于场景图和语言结构的递增式 grounding 模型 (IGSG)，通过使用场景图中的对象之间的关系，构造问答对话方式以消除人类命令的歧义和错误，该模型在现实场景中表现出可接受的性能，可以有效地通过回问式问答消除歧义的问题。

使用场景图进行增量式目标定位

Incremental Object Grounding Using Scene Graphs

The increasing availability of semantic data has substantially enhanced Web
applications. Semantic data such as RDF data is commonly represented as
entity-property-value triples. The magnitude of semantic data, in particular
the large number of triples describing an entity, could overload users with
excessive amounts of information. This has motivated fruitful research on
automated generation of summaries for entity descriptions to satisfy users'
information needs efficiently and effectively. We focus on this prominent topic
of entity summarization, and our research objective is to present the first
comprehensive survey of entity summarization research. Rather than separately
reviewing each method, our contributions include (1) identifying and
classifying technical features of existing methods to form a high-level
overview, (2) identifying and classifying frameworks for combining multiple
technical features adopted by existing methods, (3) collecting known benchmarks
for intrinsic evaluation and efforts for extrinsic evaluation, and (4)
suggesting research directions for future work. By investigating the
literature, we synthesized two hierarchies of techniques. The first hierarchy
categories generic technical features into several perspectives: frequency and
centrality, informativeness, and diversity and coverage. In the second
hierarchy we present domain-specific and task-specific technical features,
including the use of domain knowledge, context awareness, and personalization.
Our review demonstrated that existing methods are mainly unsupervised and they
combine multiple technical features using various frameworks: random surfer
models, similarity-based grouping, MMR-like re-ranking, or combinatorial
optimization. We also found a few deep learning based methods in recent
research.

本文综述了实体摘要技术的最新研究进展，探讨了现有方法的技术特征和框架，同时提出了未来研究方向。