IntelliMove: Enhancing Robotic Planning with Semantic Mapping

TL;DR

IntelliMove introduces a hierarchical semantic mapping framework and semantic planning methods to improve robotic navigation by enabling robots to understand and reason about their environments beyond basic geometry.

Contribution

The paper presents IntelliMap, a novel hierarchical semantic map framework, and Semantic Planning, a new approach for semantic navigation in robotics.

Findings

Enhanced planning efficiency and speed in simulated environments.

Effective semantic understanding for robotic navigation tasks.

Demonstrated adaptability across various use cases.

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.