# Vision-Language Model Reasoning for Contextual Semantic Mapping in Intralogistics

arXiv: https://arxiv.org/abs/2606.24814 · HTML: https://mlsanity.com/paper/2606.24814/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Spatial and embodied reasoning, one of 17 directions in LLM reasoning.
- Builds on 9 of its 33 references that are in the map.
- First posted on arXiv 23 Jun 2026.

## Placed in

- LLM reasoning / [Spatial and embodied reasoning](https://mlsanity.com/lr/spatial-and-embodied-reasoning/)
- Multimodal LLMs / vision-language models / [Embodied and 3D vision-language models](https://mlsanity.com/mllm/embodied-and-3d-vision-language-models/)

## Builds on (most cited first, 9 of 9)

- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks](https://mlsanity.com/paper/2401.14159/) (2024)
- [SAM 3: Segment Anything with Concepts](https://mlsanity.com/paper/2511.16719/) (2025)
- [MetaScenes: Towards Automated Replica Creation for Real-world 3D Scans](https://mlsanity.com/paper/2505.02388/) (2025)
- [Semantic Mapping in Indoor Embodied AI -- A Survey on Advances, Challenges, and Future Directions](https://mlsanity.com/paper/2501.05750/) (2025)
- [The Future of MLLM Prompting is Adaptive: A Comprehensive Experimental Evaluation of Prompt Engineering Methods for Robust Multimodal Performance](https://mlsanity.com/paper/2504.10179/) (2025)
- [Relationship-Aware Hierarchical 3D Scene Graph for Task Reasoning](https://mlsanity.com/paper/2602.02456/) (2026)
- [Extending Maps with Semantic and Contextual Object Information for Robot Navigation: a Learning-Based Framework using Visual and Depth Cues](https://mlsanity.com/paper/2003.06336/) (2020)
- [Real-Time Indoor Object SLAM with LLM-Enhanced Priors](https://mlsanity.com/paper/2509.21602/) (2025)

## Abstract (arXiv)

Autonomous mobile robots operating in intralogistics environments rely on geometric maps for localization and navigation, but lack semantic understanding of objects and their contextual properties. We present a contextual semantic mapping pipeline that combines SLAM-based geometric mapping, SAM-based instance segmentation, instance clustering, and VLM multi-view reasoning to produce a contextual semantic map representation encoding geometric structure, object class, and object movability. By aggregating observations across multiple viewpoints and querying a VLM in a zero-shot, open-vocabulary setting, the pipeline infers contextual object properties--here demonstrated through movability--without requiring task-specific training or predefined object categories. We evaluate three VLMs under two prompting strategies and conduct a component-wise analysis of the pipeline. The proposed pipeline achieves 98.93 % mIoU for semantic classification and 89.17 % mAcc for object movability estimation. Component analysis identifies VLM reasoning as the primary bottleneck for contextual understanding and instance clustering as the main limitation for panoptic performance. The resulting semantic map supports context-aware filtering and robust navigation in dynamic intralogistics environments.