Abstract
In order to realize fast real-time positioning after a mobile robot starts, this paper proposes an improved ORB-SLAM2 algorithm. Firstly, we proposed a binary vocabulary storage method and vocabulary training algorithm based on an improved Oriented FAST and Rotated BRIEF (ORB) operator to reduce the vocabulary size and improve the loading speed of the vocabulary and tracking accuracy. Secondly, we proposed an offline map construction algorithm based on the map element and keyframe database; then, we designed a fast reposition method of the mobile robot based on the offline map. Finally, we presented an offline visualization method for map elements and mapping trajectories. In order to check the performance of the algorithm in this paper, we built a mobile robot platform based on the EAI-B1 mobile chassis, and we implemented the rapid relocation method of the mobile robot based on improved ORB SLAM2 algorithm by using C++ programming language. The experimental results showed that the improved ORB SLAM2 system outperforms the original system regarding start-up speed, tracking and positioning accuracy, and human–computer interaction. The improved system was able to build and load offline maps, as well as perform rapid relocation and global positioning tracking. In addition, our experiment also shows that the improved system is robust against a dynamic environment.
Highlights
With the rapid development of robotics, the localization and navigation of mobile robots have attracted the attention of many scholars [1,2], and it has become a hot-spot in the field of robotics research
Because of the above disadvantages of the Oriented FAST and Rotated BRIEF (ORB)-SLAM2 system, this paper proposed a rapid relocation method for the mobile robot based on an improved ORB SLAM2 algorithm
We obtained the binary format small-scale vocabulary Fr1VOC using the method of this paper, and compared the performance between Fr1VOC and the original vocabulary BVOC of ORB-SLAM2
Summary
With the rapid development of robotics, the localization and navigation of mobile robots have attracted the attention of many scholars [1,2], and it has become a hot-spot in the field of robotics research. The localization and navigation of robots mainly rely on SLAM (Simultaneous Localization and Mapping) [3], which can conduct real-time localization and environmental reconstruction in the unknown environment. ORB-SLAM2 provides a vocabulary based on a large dataset, which enables ORB-SLAM2 to maintain high accuracy in different environments. When the working environment of the robot is relatively fixed, it still takes much time to read a large amount of invalid data in the vocabulary. ORB-SLAM2 cannot save and load maps, so the robot needs to "relearn" its work environment when it starts up every time. Because of the above disadvantages of the ORB-SLAM2 system, this paper proposed a rapid relocation method for the mobile robot based on an improved ORB SLAM2 algorithm. We designed an offline visualization method for the map and mapping trajectory of the ORB-SLAM2 system.
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