Research on Vision-based Semantic SLAM towards Indoor Dynamic Environment

Chun Yang, Tingxu Lyu
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Abstract

Most of the maps constructed based on traditional visual SLAM technology are sparse maps, which only contain geometric information and do not contain semantic information, which limits the robot to complete the tasks of understanding. In this paper, we propose a vision-based semantic SLAM method. The visual odometry is optimized by using semantic information to remove the influence of dynamic objects in the scene. Based on the proposed method, we can finally construct a semantic map. Experiments show that, our system effectively improves the positioning and mapping accuracy.
基于视觉的室内动态环境语义SLAM研究
基于传统视觉SLAM技术构建的地图大多是稀疏地图,仅包含几何信息,不包含语义信息,这限制了机器人完成理解任务。本文提出了一种基于视觉的语义SLAM方法。利用语义信息对视觉里程计进行优化,去除场景中动态物体的影响。基于所提出的方法,我们最终可以构造一个语义映射。实验表明,该系统有效地提高了定位和制图精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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