Incremental visual-inertial 3d mesh generation with structural regularities
Paper i proceeding, 2019

Visual-Inertial Odometry (VIO) algorithms typically rely on a point cloud representation of the scene that does not model the topology of the environment. A 3D mesh instead offers a richer, yet lightweight, model. Nevertheless, building a 3D mesh out of the sparse and noisy 3D landmarks triangulated by a VIO algorithm often results in a mesh that does not fit the real scene. In order to regularize the mesh, previous approaches decouple state estimation from the 3D mesh regularization step, and either limit the 3D mesh to the current frame [1], [2] or let the mesh grow indefinitely [3], [4]. We propose instead to tightly couple mesh regularization and state estimation by detecting and enforcing structural regularities in a novel factor-graph formulation. We also propose to incrementally build the mesh by restricting its extent to the time-horizon of the VIO optimization; the resulting 3D mesh covers a larger portion of the scene than a per-frame approach while its memory usage and computational complexity remain bounded. We show that our approach successfully regularizes the mesh, while improving localization accuracy, when structural regularities are present, and remains operational in scenes without regularities.


Vision-based navigation


Sensor fusion



Antoni Rosinol

Massachusetts Institute of Technology (MIT)

Torsten Sattler

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik

Marc Pollefeys

Eidgenössische Technische Hochschule Zürich (ETH)

Luca Carlone

Massachusetts Institute of Technology (MIT)

Proceedings - IEEE International Conference on Robotics and Automation

10504729 (ISSN)

Vol. 2019-May 8220-8226 8794456

2019 International Conference on Robotics and Automation, ICRA 2019
Montreal, Canada,



Datavetenskap (datalogi)

Datorseende och robotik (autonoma system)



Mer information

Senast uppdaterat