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arXiv Computer Vision By Dara Molloy, Terry Wu, Wonju Lee, Abhilash Nand Kumar, David Adley, Conor Ferguson, Hesam Rabeti

Line-Augmented cuVSLAM: Robust Visual Odometry in Low-Texture Environments

Read the original on arXiv Computer Vision →

Line-Augmented cuVSLAM extends the CUDA-accelerated visual‑SLAM system cuVSLAM by incorporating line features to improve visual odometry in low‑texture environments such as warehouses and production facilities. Lines are represented using Plücker coordinates for transport and a minimal 4‑DOF orthonormal form for optimisation, and a Cauchy‑robust point‑to‑line residual is integrated into the per‑frame Gauss‑Newton pose solve and sliding‑window bundle adjustment. The adaptive weighting allows line features to take on more of the estimation burden when point features are scarce, and the extension adds residual blocks to existing solvers rather than creating a separate estimator.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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