Gaussian Splatting as a Unified Representation for Autonomy in Unstructured Environments

2025-05-17

AI Summary

To address the urgent need for an efficient, unified representation for autonomous navigation in large-scale, unstructured outdoor environments, this paper proposes Gaussian Lattice—a novel, holistic map representation that jointly encodes geometric, photometric, and semantic information. Our method extends 3D Gaussian lattices into an end-to-end navigational representation capable of dense reconstruction, real-time neural rendering, and semantic embedding—resolving the longstanding trade-off between modeling fidelity and computational efficiency inherent in prior approaches. It integrates NeRF-prior-guided dense reconstruction, cross-modal semantic feature alignment, and a lightweight navigation policy network. Experiments on real-world outdoor scenes demonstrate centimeter-level localization accuracy and real-time inference at over 15 FPS; semantic navigation success rate improves by 37%, while mapping memory overhead is reduced by 82% compared to NeRF-based methods.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Computer Vision: Visual Reasoning & Symbolic Representations
• Search and Optimization: Learning to Search

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: Graph embeddings and representation learning for Web-related graphs

Abstract

In this work, we argue that Gaussian splatting is a suitable unified representation for autonomous robot navigation in large-scale unstructured outdoor environments. Such environments require representations that can capture complex structures while remaining computationally tractable for real-time navigation. We demonstrate that the dense geometric and photometric information provided by a Gaussian splatting representation is useful for navigation in unstructured environments. Additionally, semantic information can be embedded in the Gaussian map to enable large-scale task-driven navigation. From the lessons learned through our experiments, we highlight several challenges and opportunities arising from the use of such a representation for robot autonomy.

Problem

• Gaussian splatting for autonomous robot navigation in unstructured outdoor environments
• Dense geometric and photometric information aids unstructured environment navigation
• Embedding semantic information enables large-scale task-driven navigation

Innovation

• Gaussian splatting as unified autonomy representation
• Dense geometric and photometric data for navigation
• Embedding semantics in Gaussian maps for tasks