Map Imagination Like Blind Humans: Group Diffusion Model for Robotic Map Generation
This addresses the challenge of sensor dependency in robotics, allowing for map generation without heavy onboard sensory devices, which is incremental as it builds on diffusion models for a specific domain.
The paper tackles the problem of enabling robots to generate maps with very limited input information, akin to how blind humans imagine mental maps, by proposing a group diffusion model (GDM) that can generate point cloud maps based solely on path data, with quality improving when incorporating exiguous LiDAR data.
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.