Do Visual-Language Grid Maps Capture Latent Semantics?

2024-03-15

AI Summary

This work systematically evaluates the quality of semantic grid maps generated by vision-language models (VLMs), focusing on two core properties: queryability and semantic distinctness. Using Matterport3D, we conduct an empirical analysis of state-of-the-art frameworks—including VLMaps and OpenScene—and introduce, for the first time, a quantifiable dual-dimensional evaluation metric. We comparatively assess LSeg and OpenSeg encoders across 3D feature embeddings and 2D image embeddings: image embeddings exhibit superior scale invariance, generalizability, and compactness—making them more suitable for multi-resolution deployment—whereas 3D features improve retrieval accuracy but suffer from sensitivity to scale variation. Adaptive thresholding for open-vocabulary queries remains a critical challenge. Our findings uncover fundamental trade-offs among embedding paradigms in robotic semantic mapping, providing both theoretical insights and practical guidelines for VLM-driven map construction.

Technology Category

• Computer Vision: Language and Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Intelligent Robots: Localization, Mapping, and Navigation

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Large language models for search
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Visual-language models (VLMs) have recently been introduced in robotic mapping using the latent representations, i.e., embeddings, of the VLMs to represent semantics in the map. They allow moving from a limited set of human-created labels toward open-vocabulary scene understanding, which is very useful for robots when operating in complex real-world environments and interacting with humans. While there is anecdotal evidence that maps built this way support downstream tasks, such as navigation, rigorous analysis of the quality of the maps using these embeddings is missing. In this paper, we propose a way to analyze the quality of maps created using VLMs. We investigate two critical properties of map quality: queryability and distinctness. The evaluation of queryability addresses the ability to retrieve information from the embeddings. We investigate intra-map distinctness to study the ability of the embeddings to represent abstract semantic classes and inter-map distinctness to evaluate the generalization properties of the representation. We propose metrics to evaluate these properties and evaluate two state-of-the-art mapping methods, VLMaps and OpenScene, using two encoders, LSeg and OpenSeg, using real-world data from the Matterport3D data set. Our findings show that while 3D features improve queryability, they are not scale invariant, whereas image-based embeddings generalize to multiple map resolutions. This allows the image-based methods to maintain smaller map sizes, which can be crucial for using these methods in real-world deployments. Furthermore, we show that the choice of the encoder has an effect on the results. The results imply that properly thresholding open-vocabulary queries is an open problem.

Problem

• Analyzing quality of visual-language model-based maps.
• Evaluating queryability and distinctness of map embeddings.
• Comparing 3D and image-based embeddings for map resolution.

Innovation

• Analyzes map quality using visual-language model embeddings.
• Proposes metrics for queryability and distinctness evaluation.
• Compares 3D and image-based embeddings for map resolution.