DGFlow-SLAM: A Novel Dynamic Environment RGB-D SLAM without Prior Semantic Knowledge Based on Grid Segmentation of Scene Flow.
DGFlow-SLAM: A Novel Dynamic Environment RGB-D SLAM without Prior Semantic Knowledge Based on Grid Segmentation of Scene Flow.
复制标题
DOI：
10.3390/biomimetics7040163
复制
发表时间：
2022-10-13
期刊：
影响因子：
--
通讯作者：
中科院分区：
文献类型：
--
作者：
目前，利用语义分割网络区分动态关键点和静态关键点已成为语义SLAM系统的主流设计方法。然而，语义SLAM系统必须具有相关动态对象的先验语义知识，其处理速度与识别准确率成反比。为了同时提高动态物体在不同环境中的识别速度和准确率，提出了一种新的无先验语义的SLAM系统DGFlow-SLAM。该系统采用一种新颖的网格分割方法对场景流进行分割，然后采用自适应阈值方法对动态目标进行粗略检测。在此基础上，采用深度均值聚类分割方法寻找潜在的动态目标。最后，结合网格分割和深度均值聚类分割的结果，准确地找到运动目标，在保留运动目标静止部分的前提下，去除运动目标的所有特征点。实验结果表明，在TUM RGB-D动态序列数据集上，DGflow-SLAM系统检测中、剧烈运动的准确率最高，DS-SLAM系统检测轻微运动的准确率最高，DGflow-SLAM的准确率与DGflow-SLAM相似，准确率提高7.5%。另外，DGflow-SLAM算法的运行速度是DyaSLAM算法的10倍，DS-SLAM算法的1.27倍。
Currently, using semantic segmentation networks to distinguish dynamic and static key points has become a mainstream designing method for semantic SLAM systems. However, the semantic SLAM systems must have prior semantic knowledge of relevant dynamic objects, and their processing speed is inversely proportional to the recognition accuracy. To simultaneously enhance the speed and accuracy for recognizing dynamic objects in different environments, a novel SLAM system without prior semantics called DGFlow-SLAM is proposed in this paper. A novel grid segmentation method is used in the system to segment the scene flow, and then an adaptive threshold method is used to roughly detect the dynamic objects. Based on this, a deep mean clustering segmentation method is applied to find potential dynamic targets. Finally, the results of grid segmentation and depth mean clustering segmentation are jointly used to find moving objects accurately, and all the feature points of the moving objects are removed on the premise of retaining the static part of the moving object. The experimental results show that on the dynamic sequence dataset of TUM RGB-D, compared with the DynaSLAM system with the highest accuracy for detecting moderate and violent motion and the DS-SLAM with the highest accuracy for detecting slight motion, DGflow-SLAM obtains similar accuracy results and improves the accuracy by 7.5%. In addition, DGflow-SLAM is 10 times and 1.27 times faster than DynaSLAM and DS-SLAM, respectively.
登录
查看更多内容
DOI：
10.1109/tpami.2017.2658577
发表时间：
2018-03-01
影响因子：
23.6
作者：
Engel, Jakob;Koltun, Vladlen;Cremers, Daniel
通讯作者：
Cremers, Daniel
影响因子：
2.7
作者：
Barber, CB;Dobkin, DP;Huhdanpaa, H
通讯作者：
Huhdanpaa, H
影响因子：
3.7
作者：
ROUSSEEUW, PJ
通讯作者：
ROUSSEEUW, PJ
影响因子：
5.2
作者：
Li, Shile;Lee, Dongheui
通讯作者：
Lee, Dongheui
影响因子：
3.9
作者：
Hempel, Thorsten;Al-Hamadi, Ayoub
通讯作者：
Al-Hamadi, Ayoub