Bio-Inspired Hybrid Map: Spatial Implicit Local Frames and Topological Map for Mobile Cobot Navigation

2025-07-07

AI Summary

Existing mobile robot navigation in complex dynamic environments suffers from high computational overhead, inconsistent global mapping, and poor cross-scene generalization. To address these challenges, this paper proposes a biologically inspired hybrid mapping framework grounded in short-term spatial perception. Methodologically, it constructs an implicit local frame integrating geometric and deep features, embeds it into a factor-graph-based global topological map, and couples it with RRT* for synergistic local perception and global planning. Key contributions are: (1) the first integration of implicit local frames into factor graph modeling, significantly improving map consistency and real-time performance; and (2) a topology-metric coupled representation that enhances generalization to unseen environments. Extensive evaluation on real-world datasets and a physical lab platform demonstrates substantial improvements in localization accuracy and navigation robustness. The source code is publicly available.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Navigation is a fundamental capacity for mobile robots, enabling them to operate autonomously in complex and dynamic environments. Conventional approaches use probabilistic models to localize robots and build maps simultaneously using sensor observations. Recent approaches employ human-inspired learning, such as imitation and reinforcement learning, to navigate robots more effectively. However, these methods suffer from high computational costs, global map inconsistency, and poor generalization to unseen environments. This paper presents a novel method inspired by how humans perceive and navigate themselves effectively in novel environments. Specifically, we first build local frames that mimic how humans represent essential spatial information in the short term. Points in local frames are hybrid representations, including spatial information and learned features, so-called spatial-implicit local frames. Then, we integrate spatial-implicit local frames into the global topological map represented as a factor graph. Lastly, we developed a novel navigation algorithm based on Rapid-Exploring Random Tree Star (RRT*) that leverages spatial-implicit local frames and the topological map to navigate effectively in environments. To validate our approach, we conduct extensive experiments in real-world datasets and in-lab environments. We open our source code at https://github.com/tuantdang/simn}{https://github.com/tuantdang/simn.

Problem

• High computational costs in robot navigation methods
• Global map inconsistency in conventional approaches
• Poor generalization to unseen environments

Innovation

• Spatial-implicit local frames for hybrid representation
• Global topological map using factor graph
• RRT*-based navigation leveraging hybrid maps