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

Jiawei Hou, Wenhao Guan, Longfei Liang, Xiangyang Xue, Taiping Zeng

Keywords: Robotic scene understanding, Neural scene representation, Hierarchical representation, Topometric map

Abstract:

Mobile robots require comprehensive scene understanding to operate effectively in diverse environments, enriched with contextual information such as layouts, objects, and their relationships. While advancements like Neural Radiance Fields (NeRF) 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 generated by LiDAR or visual SLAM methods are computationally efficient but lack the semantic richness necessary for a more complete understanding of the environment.
Inspired by neuroscientific studies on spatial cognition, particularly the role of postrhinal cortex (POR) neurons that are strongly tuned to spatial layouts over scene content, 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 NeRF, offering both semantic richness and computational efficiency.
Empirical evaluations in multi-room apartment 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.

Primary Area: applications to robotics, autonomy, planning

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Submission Number: 2902