Comparativo de algoritmos de localização e mapeamento simultâneos monoculares dynaslam e ORB-SLAM3 em ambientes internos e externos

Abstract

This work focuses on comparing two visual Simultaneous Localization and Mapping (vSLAM) algorithms: ORB-SLAM3 and DynaSLAM, utilizing simulations with a monocular camera. ORB-SLAM3 is an open-source library known for its monocular vSLAM capabilities and is an evolution of the well-regarded ORB-SLAM2. In contrast, DynaSLAM extends ORB-SLAM2 by incorporating Mask R-CNN for dynamic object detection, filtering, and segmentation. Both algorithms were tested and evaluated using sequences of monocular images from two popular datasets, KITTI and TUM RGB-D as well as simulation environments with ROS on the Gazebo 3D software. The experiments demonstrate the efficiency of the vSLAM algorithms. The results reveal that DynaSLAM consistently outperforms ORBSLAM3 in the majority of cases. Overall, this research contributes to the understanding of these vSLAM methods, providing insights into their performance and highlighting the advantages and disadvantages of DynaSLAM over ORB-SLAM3 in various scenarios.