F-LIVO: A Feature-Level Geometric Measurement Framework for LiDAR-Inertial-Visual Odometry

Key points

• Persistently attaching dense photometric patches or appearance attributes to mapped LiDAR points can introduce additional storage, association, and update costs.1
• This paper presents F-LIVO, a filter-based LIVO framework that couples a visual-inertial subsystem and a LiDAR-inertial subsystem through feature-level point-to-plane constraints between visual points and LiDAR planes.1
• To preserve cross-modal coupling when visual triangulation is weak, we further recover visual feature depth from the LiDAR map through local plane fitting and ray-plane intersection.1
• In the LiDAR update, the covariance of each point-to-plane residual is adaptively scaled according to local planar reliability, thereby reducing overconfident updates on poorly conditioned surfaces.1

Sources (1)

• [1]F-LIVO: A Feature-Level Geometric Measurement Framework for LiDAR-Inertial-Visual Odometry
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 16, 03:14 PM

  Measurement-level LiDAR-Inertial-Visual Odometry (LIVO) can achieve high accuracy, and its practical resource requirements depend on the adopted association and map-maintenance strategy.

  Persistently attaching dense photometric patches or appearance attributes to mapped LiDAR points can introduce additional storage, association, and update costs.

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