Event-based Mosaicing Bundle Adjustment

• URL: http://arxiv.org/abs/2409.07365v1
• Date: Wed, 11 Sep 2024 15:53:01 GMT
• Title: Event-based Mosaicing Bundle Adjustment
• Authors: Shuang Guo, Guillermo Gallego,
• Abstract summary: We tackle the problem of mosaicing bundle adjustment (i.e., simultaneous refinement of camera orientations and scene map) for a purely rotating event camera. We show that this BA optimization has an exploitable block-diagonal sparsity structure, so that the problem can be solved efficiently. We evaluate our method, called EMBA, on both synthetic and real-world datasets to show its effectiveness.
• Score: 12.504055397619727
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We tackle the problem of mosaicing bundle adjustment (i.e., simultaneous refinement of camera orientations and scene map) for a purely rotating event camera. We formulate the problem as a regularized non-linear least squares optimization. The objective function is defined using the linearized event generation model in the camera orientations and the panoramic gradient map of the scene. We show that this BA optimization has an exploitable block-diagonal sparsity structure, so that the problem can be solved efficiently. To the best of our knowledge, this is the first work to leverage such sparsity to speed up the optimization in the context of event-based cameras, without the need to convert events into image-like representations. We evaluate our method, called EMBA, on both synthetic and real-world datasets to show its effectiveness (50% photometric error decrease), yielding results of unprecedented quality. In addition, we demonstrate EMBA using high spatial resolution event cameras, yielding delicate panoramas in the wild, even without an initial map. Project page: https://github.com/tub-rip/emba

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