首页> 外文会议>IEEE/RSJ International Conference on Intelligent Robots and Systems >Submap-Based Pose-Graph Visual SLAM: A Robust Visual Exploration and Localization System* The work in this paper is supported by the National Natural Science Foundation of China (61603103, 61673125), the Natural Science Foundation of Guangdong of China (2
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Submap-Based Pose-Graph Visual SLAM: A Robust Visual Exploration and Localization System* The work in this paper is supported by the National Natural Science Foundation of China (61603103, 61673125), the Natural Science Foundation of Guangdong of China (2

机译:基于子图的姿势图视觉SLAM:强大的视觉探索和定位系统*本文的工作得到中国国家自然科学基金(61603103,61673125),中国广东省自然科学基金(2)的支持

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For VSLAM (Visual Simultaneous Localization and Mapping), localization is a challenging task, especially for some challenging situations: textureless frames, motion blur, etc. To build a robust exploration and localization system in a given space, a submap-based VSLAM system is proposed in this paper. Our system uses a submap back-end and a visual front-end. The main advantage of our system is its robustness with respect to tracking failure, a common problem in current VSLAM algorithms. The robustness of our system is compared with the state-of-the-art in terms of average tracking percentage. The precision of our system is also evaluated in terms of ATE (absolute trajectory error) RMSE (root mean square error) comparing the state-of-the-art. The ability of our system in solving the “kidnapped” problem is demonstrated. Our system can improve the robustness of visual localization in challenging situations.
机译:对于VSLAM(可视化同时定位和制图)而言,定位是一项艰巨的任务,尤其是在某些极具挑战性的情况下:无纹理帧,运动模糊等。要在给定空间中构建强大的探索和定位系统,则需要使用基于子图的VSLAM系统。本文提出。我们的系统使用子图后端和可视前端。我们系统的主要优点是相对于跟踪失败的鲁棒性,这是当前VSLAM算法中的常见问题。就平均跟踪百分比而言,我们的系统的健壮性与最新技术进行了比较。我们还根据最新技术的ATE(绝对轨迹误差)RMSE(均方根误差)评估了我们系统的精度。证明了我们系统解决“绑架”问题的能力。我们的系统可以在挑战性环境中提高视觉定位的鲁棒性。

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