ボクセルマップを用いたイベントベースステレオ視覚慣性オドメトリ
Event-based Stereo Visual-Inertial Odometry with Voxel Map
イベントカメラのノイズに強い高品質な3D点をボクセル単位で選別・管理することで、ステレオ視覚慣性オドメトリの精度と計算効率を向上させた。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Zhaoxing Zhang, Xiaoxiang Wang, Chengliang Zhang, Yangyang Guo, Zikang Yuan, Xin Yang
分類: cs.RO
原文アブストラクト
The event camera, renowned for its high dynamic range and exceptional temporal resolution, is recognized as an important sensor for visual odometry. However, the inherent noise in event streams complicates the selection of high-quality map points, which critically determine the precision of state estimation. To address this challenge, we propose Voxel-ESVIO, an event-based stereo visual-inertial odometry system that utilizes voxel map management, which efficiently filter out high-quality 3D points. Specifically, our methodology utilizes voxel-based point selection and voxel-aware point management to collectively optimize the selection and updating of map points on a per-voxel basis. These synergistic strategies enable the efficient retrieval of noise-resilient map points with the highest observation likelihood in current frames, thereby ensureing the state estimation accuracy. Extensive evaluations on three public benchmarks demonstrate that our Voxel-ESVIO outperforms state-of-the-art methods in both accuracy and computational efficiency.
