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English(EN) A Stereo Visual SLAM System Using Object-Level Motion Estimation and Geometric Filtering Based on Cross Disparity

新的SLAM系统提高了动态环境下的精度

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了OCD SLAM，一个新颖的立体视觉SLAM框架，旨在提高动态环境下的精度。该系统通过引入物体级运动估计和基于交叉视差的几何滤波技术来增强ORB-SLAM2框架。该方法能有效区分静态和动态场景元素，从而实现更鲁棒的姿态估计和建图。在KITTI数据集上的评估表明，OCD SLAM在轨迹精度方面优于ORB-SLAM2和其他最先进的动态SLAM方法。 AI

排序理由 该集群包含一篇详细介绍新SLAM系统的研究论文。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

新的SLAM系统提高了动态环境下的精度

报道来源 [2]

  1. arXiv cs.CV TIER_1 English(EN) · Sujan Kumar Dhali, Bhaskar Dasgupta ·

    A Stereo Visual SLAM System Using Object-Level Motion Estimation and Geometric Filtering Based on Cross Disparity

    arXiv:2607.02005v1 Announce Type: cross Abstract: This paper presents OCD SLAM, a dynamic stereo visual SLAM framework that extends ORB-SLAM2 by jointly addressing dynamic objects and dynamic features in the scene. Usual visual SLAM systems operating in dynamic environments often…

  2. arXiv cs.CV TIER_1 English(EN) · Bhaskar Dasgupta ·

    A Stereo Visual SLAM System Using Object-Level Motion Estimation and Geometric Filtering Based on Cross Disparity

    This paper presents OCD SLAM, a dynamic stereo visual SLAM framework that extends ORB-SLAM2 by jointly addressing dynamic objects and dynamic features in the scene. Usual visual SLAM systems operating in dynamic environments often fail in the presence of moving objects, due to th…

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