Topo-Field: Topometric mapping with Brain-inspired Hierarchical Layout-Object-Position Fields

TL;DR

Topo-Field introduces a brain-inspired hierarchical neural mapping framework that combines semantic scene understanding with efficient topometric representations, enhancing robot navigation and environment comprehension.

Contribution

It presents a novel neural field-based topometric mapping approach integrating layout-object-position associations inspired by the POR, enabling efficient and semantically rich environment modeling.

Findings

Effective in multi-room environments for position inference

Supports query localization and topometric planning

Bridges high-fidelity scene understanding with navigation efficiency

Abstract

Mobile robots require comprehensive scene understanding to operate effectively in diverse environments, enriched with contextual information such as layouts, objects, and their relationships. Although advances like neural radiation fields (NeRFs) offer high-fidelity 3D reconstructions, they are computationally intensive and often lack efficient representations of traversable spaces essential for planning and navigation. In contrast, topological maps are computationally efficient but lack the semantic richness necessary for a more complete understanding of the environment. Inspired by a population code in the postrhinal cortex (POR) strongly tuned to spatial layouts over scene content rapidly forming a high-level cognitive map, this work introduces Topo-Field, a framework that integrates Layout-Object-Position (LOP) associations into a neural field and constructs a topometric map from this learned representation. LOP associations are modeled by explicitly encoding object and layout information, while a Large Foundation Model (LFM) technique allows for efficient training without extensive annotations. The topometric map is then constructed by querying the learned neural representation, offering both semantic richness and computational efficiency. Empirical evaluations in multi-room environments demonstrate the effectiveness of Topo-Field in tasks such as position attribute inference, query localization, and topometric planning, successfully bridging the gap between high-fidelity scene understanding and efficient robotic navigation.