NextBestPath

NextBestPath

Efficient 3D Mapping of Unseen Environments

Shiyao Li1,2     Antoine Guédon1     Clémentin Boittiaux1     Shizhe Chen2     Vincent Lepetit1    

ICLR 2025

1École Nationale des Ponts et Chaussées     2Inria    
Paper Code (Coming soon) Dataset and Toolkit

Left: Previous state of the art: MACARONS (see Related Projects).

Right: Our method.

NextBestPath enables the agent to perform efficient active mapping in unknown and complex indoor environments.

Overview

  • We introduce AiMDoom, the first benchmark to systematically evaluate active mapping in indoor scenes of different levels of difficulties.
  • We propose a novel next-best-path approach that jointly predicts long-term goals with optimal reconstruction coverage gains, and obstacle maps for trajectory planning.
  • Our approach achieved state-of-the-art results on both the AiMDoom and MP3D datasets.

AiMDoom Dataset

Random maps from the AiMDoom dataset, categorized into four levels by scene size and complexity, with 100 scenes per level, arranged from top to bottom: Simple, Normal, Hard, and Insane.

Architecture of NextBestPath

Overview of the next-best-path (NBP) framework. The model predicts a value map of coverage gain and an obstacle map, which are used for decision making to obtain a next-best path.

More comparisons (more visualizations are coming soon)

Previous state of the art

Our method

Previous works mainly focus on using reinforcement learning (RL) or uncertainty prediction methods.

We improve the NBV-based SOTA method MACARONS with RL (left), but it still underperforms our approach by a large margin (right).


BibTeX

@article{li2025nextbestpath,
      title={NextBestPath: Efficient 3D Mapping of Unseen Environments},
      author={Li, Shiyao and Gu{\'e}don, Antoine and Boittiaux, Cl{\'e}mentin and Chen, Shizhe and Lepetit, Vincent},
      journal={arXiv preprint arXiv:2502.05378},
      year={2025}
    }

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