IntelliMove: Enhancing Robotic Planning with Semantic Mapping

TL;DR

Two core modules of IntelliMove are introduced: IntelliMap, a generic hierarchical semantic topometric map framework developed through an analysis of current technologies strengths and weaknesses, and Semantic Planning, which utilizes the semantic maps from IntelliMap, which utilizes the semantic maps from IntelliMap.

Abstract

Semantic navigation enables robots to understand their environments beyond basic geometry, allowing them to reason about objects, their functions, and their interrelationships. In semantic robotic navigation, creating accurate and semantically enriched maps is fundamental. Planning based on semantic maps not only enhances the robot's planning efficiency and computational speed but also makes the planning more meaningful, supporting a broader range of semantic tasks. In this paper, we introduce two core modules of IntelliMove: IntelliMap, a generic hierarchical semantic topometric map framework developed through an analysis of current technologies strengths and weaknesses, and Semantic Planning, which utilizes the semantic maps from IntelliMap. We showcase use cases that highlight IntelliMove's adaptability and effectiveness. Through experiments in simulated environments, we further demonstrate IntelliMove's capability in semantic navigation.

Figures (3)

• Figure 1: IntelliMap framework for semantic robot navigation. This figure illustrates the core components of the IntelliMap framework, designed to generate hierarchical semantic topometric maps for robot navigation in diverse environments.
• Figure 2: Three-Layered IntelliMap in a simulated office environment
• Figure 3: Example of a red semantic path generated in an office environment, navigating from the bookcase in office room 1 to a desk in office room 3.