Map Imagination Like Blind Humans: Group Diffusion Model for Robotic Map Generation

TL;DR

This paper introduces a group diffusion model enabling robots to generate maps from limited path data, mimicking human mental map creation, and reducing reliance on extensive sensory inputs.

Contribution

The paper presents a novel group diffusion model architecture that allows robots to generate maps with minimal sensory data, inspired by human mental map abilities.

Findings

The method produces reasonable maps from path data alone.

Incorporating sparse LiDAR data refines map quality.

The approach reduces sensor dependency compared to traditional methods.

Abstract

Can robots imagine or generate maps like humans do, especially when only limited information can be perceived like blind people? To address this challenging task, we propose a novel group diffusion model (GDM) based architecture for robots to generate point cloud maps with very limited input information.Inspired from the blind humans' natural capability of imagining or generating mental maps, the proposed method can generate maps without visual perception data or depth data. With additional limited super-sparse spatial positioning data, like the extra contact-based positioning information the blind individuals can obtain, the map generation quality can be improved even more.Experiments on public datasets are conducted, and the results indicate that our method can generate reasonable maps solely based on path data, and produce even more refined maps upon incorporating exiguous LiDAR data.Compared to conventional mapping approaches, our novel method significantly mitigates sensor dependency, enabling the robots to imagine and generate elementary maps without heavy onboard sensory devices.