Research on Indoor 3D Semantic Mapping Based on ORB-SLAM2 and Multi-Object Tracking

Abstract

The integration of semantic simultaneous localization and mapping (SLAM) with 3D object detection in indoor scenes is a significant challenge in the field of robot perception. Existing methods typically rely on expensive sensors and lack robustness and accuracy in complex environments. To address this, this paper proposes a novel 3D semantic SLAM framework that integrates Oriented FAST and Rotated BRIEF-SLAM2 (ORB-SLAM2), 3D object detection, and multi-object tracking (MOT) techniques to achieve efficient and robust semantic environment modeling. Specifically, we employ an improved 3D object detection network to extract semantic information and enhance detection accuracy through category balancing strategies and optimized loss functions. Additionally, we introduce MOT algorithms to filter and track 3D bounding boxes, enhancing stability in dynamic scenes. Finally, we deeply integrate 3D semantic information into the SLAM system, achieving high-precision 3D semantic map construction. Experiments were conducted on the public dataset SUNRGBD and two self-collected datasets (robot navigation and XR glasses scenes). The results show that, compared with the current state-of-the-art methods, our method demonstrates significant advantages in detection accuracy, localization accuracy, and system robustness, providing an effective solution for low-cost, high-precision indoor semantic SLAM.