RoMo-GS: Efficient Online 3D Gaussian Splatting for Robot Mapping via Exposure-Integrated Motion Blur

Key points

• Rapid maneuvers of a robot induce motion-blurred images, which corrupt the reconstructed geometry and cause camera pose drift.1
• In this study, we present RoMo-GS, an online 3DGS system designed to address these challenges.1
• Instead of averaging N discrete sub-frames as in prior motion blur-aware 3DGS, we analytically integrate each projected Gaussian over the exposure interval, yielding an O(1) complexity per Gaussian.1
• Building on this, we bound the Gaussians' growth with a Hessian-based pruning score, while jointly refining pose, velocity, and Gaussians with Adam and an online LM step over the Gaussians.1

Sources (1)

• [1]RoMo-GS: Efficient Online 3D Gaussian Splatting for Robot Mapping via Exposure-Integrated Motion Blur
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 17, 10:15 AM

  Online 3D Gaussian Splatting (3DGS) is emerging as a practical way to build a representation for robotics applications.

  Rapid maneuvers of a robot induce motion-blurred images, which corrupt the reconstructed geometry and cause camera pose drift.

Extractive summary: sentences quoted from the sources.