We present an optimization study of the Vision-Language Frontier Maps (VLFM) applied to the Object Goal Navigation task in robotics. Our work evaluates the efficiency and performance of various vision-language models, object detectors, segmentation models, and multi-modal comprehension and Visual Question Answering modules. Using the $\textit{val-mini}$ and $\textit{val}$ splits of Habitat-Matterport 3D dataset, we conduct experiments on a desktop with limited VRAM. We propose a solution that achieves a higher success rate (+1.55%) improving over the VLFM BLIP-2 baseline without substantial success-weighted path length loss while requiring $\textbf{2.3 times}$ less video memory. Our findings provide insights into balancing model performance and computational efficiency, suggesting effective deployment strategies for resource-limited environments.

翻译：本文对视觉-语言边界地图（VLFM）在机器人目标导航任务中的应用进行了优化研究。我们评估了多种视觉-语言模型、目标检测器、分割模型以及多模态理解与视觉问答模块的效率与性能。基于Habitat-Matterport 3D数据集的$\textit{val-mini}$和$\textit{val}$子集，我们在显存受限的台式机上开展实验。提出了一种解决方案，在无需显著牺牲成功率加权路径长度的前提下，相较于VLFM BLIP-2基线将成功率提升1.55%，同时所需视频内存减少$\textbf{2.3倍}$。我们的研究结果为平衡模型性能与计算效率提供了见解，并提出了适用于资源受限环境的有效部署策略。