Next-Best-View selection from observation viewpoint statistics

Stéphanie Aravecchia (1) , Antoine Richard (2) , Marianne Clausel (3) , Cédric Pradalier (1)

1 Georgia Tech Lorraine [Metz]
2 University of Luxembourg [Luxembourg]
3 IECL - Institut Élie Cartan de Lorraine

This paper discusses the problem of autonomously constructing a qualitative map of an unknown 3D environment using a 3D-Lidar. In this case, how can we effectively integrate the quality of the 3D-reconstruction into the selection of the Next-Best-View? Here, we address the challenge of estimating the quality of the currently reconstructed map in order to guide the exploration policy, in the absence of ground truth, which is typically the case in exploration scenarios. Our key contribution is a method to build a prior on the quality of the reconstruction from the data itself. Indeed, we not only prove that this quality depends on statistics from the observation viewpoints, but we also demonstrate that we can enhance the quality of the reconstruction by leveraging these statistics during the exploration. To do so, we propose to integrate them into Next-Best-View selection policies, in which the information gain is directly computed based on these statistics. Finally, we demonstrate the robustness of our approach, even in challenging environments, with noise in the robot localization, and we further validate it through a real-world experiment.

Mots clés: next-best-view; NBV; exploration

Domaines: Robotique [cs.RO]

Stéphanie Aravecchia, Antoine Richard, Marianne Clausel, Cédric Pradalier. Next-Best-View selection from observation viewpoint statistics. International Conference on Intelligent Robots and Systems (IROS), IEEE, Oct 2023, Detroit, Michigan/USA, United States. pp.10505-10510, ⟨10.1109/IROS55552.2023.10341982⟩. ⟨hal-04128252⟩