Mobile robots require comprehensive scene understanding to operate effectively in diverse environments, enriched with contextual information such as layouts, objects, and their relationships.
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.
Pipeline of the Topo-Field. (a) The ground truth generation of layout-object-position vision-language and semantic embeddings for weakly-supervising. (b) The neural implicit network mapping 3D positions to target feature space. A contrastive loss is optimized against each other. (c) Topometric mapping process with trained neural field.
@inproceedings{hou2024Topo-field,
title={Topo-Field: Topometric mapping with Brain-inspired Hierarchical Layout-Object-Position Fields},
author={Hou, Jiawei and Guan, Wenhao and Liang, Longfei and Feng, Jianfeng and Xue, Xiangyang and Zeng, Taiping},
booktitle = {arXiv},
year = {2024},
}