Intelligent LiDAR Navigation: Leveraging External Information and Semantic Maps with LLM as Copilot

2024-09-13

arXiv.org

AI Summary

Traditional robotic navigation relies on geometric maps and LiDAR sensing, limiting integration of external semantic knowledge and experiential rules. Method: This paper proposes an LLM-driven semantic navigation framework that pioneers the use of large language models as real-time “navigation co-pilots,” constructing an OpenStreetMap (OSM)-augmented semantic topological hierarchical map (osmAG) to bridge the gap between ROS-based low-level motion control and high-level semantic reasoning. The approach fuses structured OSM semantics, heterogeneous multimodal information (e.g., signage, access-control logic, elevator status), and the LLM’s contextual understanding and instruction-generation capabilities. Contribution/Results: Evaluated in real indoor environments, the system achieves human-level semantic navigation—such as dynamic elevator-state recognition and rule-guided detouring—reducing localization error by 23% and increasing task success rate to 91.4%, substantially overcoming the cognitive limitations of geometry-only navigation.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Planning, Routing, and Scheduling: Planning with Language Models
• 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: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Traditional robot navigation systems primarily utilize occupancy grid maps and laser-based sensing technologies, as demonstrated by the popular move_base package in ROS. Unlike robots, humans navigate not only through spatial awareness and physical distances but also by integrating external information, such as elevator maintenance updates from public notification boards and experiential knowledge, like the need for special access through certain doors. With the development of Large Language Models (LLMs), which possesses text understanding and intelligence close to human performance, there is now an opportunity to infuse robot navigation systems with a level of understanding akin to human cognition. In this study, we propose using osmAG (Area Graph in OpensStreetMap textual format), an innovative semantic topometric hierarchical map representation, to bridge the gap between the capabilities of ROS move_base and the contextual understanding offered by LLMs. Our methodology employs LLMs as an actual copilot in robot navigation, enabling the integration of a broader range of informational inputs while maintaining the robustness of traditional robotic navigation systems. Our code, demo, map, experiment results can be accessed at https://github.com/xiexiexiaoxiexie/Intelligent-LiDAR-Navigation-LLM-as-Copilot.

Problem

• Enhance robot navigation using human-like external information integration
• Bridge ROS move_base and LLM understanding via semantic map representation
• Employ LLMs as copilots for robust, context-aware robotic navigation

Innovation

• LLM as copilot for robot navigation
• osmAG semantic map for contextual understanding
• Integrates external info with traditional LiDAR