Target localization in local dense mapping using RGBD SLAM and object detection. (30th September 2021)

• Record Type: Journal Article
• Authors: Liu, Yuting; Xu, Manman; Jiang, Guozhang; Tong, Xiliang; Yun, Juntong; Liu, Ying; Chen, Baojia; Cao, Yongcheng; Sun, Nannan; Li, Zeshen
• Is Part Of: Concurrency and computation. Volume 34:Number 4(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-09-30
• Subjects: object detection -- point cloud mapping -- SLAM -- target localization; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.6655
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: Target localization in unknown environment is one of the development directions of mobile robots. Simultaneous localization and mapping (SLAM) can be used to build maps in unknown environments, but it has the problem of poor readability and interactivity. In this article, target detection and SLAM are combined to search and locate the target by using rich RGBD images information. The determined position in the global map is conducive to the follow‐up operation of the target by mobile robots. By establishing a local dense point cloud map of the target object, the current state of the target object is directly displayed, the readability of the map is improved, and the disadvantages of difficult understanding of the global sparse map and slow construction of the global dense map are avoided. A target localization algorithm under the framework of yolov4 is designed to apply in the process of SLAM global mapping. Our works are helpful for obtaining positions of objects in three‐dimensional space. The experimental results show that the time‐consuming of this method in dense mapping is reduced by 50%–70%, and the number of point clouds is also reduced by 60%–70%.