PLD-SLAM: A Real-Time Visual SLAM Using Points and Line Segments in Dynamic Scenes
This addresses practical challenges in visual SLAM for robotics and AR/VR applications, but it is incremental as it builds on existing SLAM methods with hybrid features and a new algorithm for keyframe selection.
The paper tackles the problem of achieving stable and accurate pose estimation in low-texture and dynamic environments for visual SLAM, proposing PLD-SLAM which combines point and line features and uses a global gray similarity algorithm, resulting in better real-time performance while maintaining stability and accuracy compared to SOTA methods on datasets like KITTI and EuRoC MAV.
In this paper, we consider the problems in the practical application of visual simultaneous localization and mapping (SLAM). With the popularization and application of the technology in wide scope, the practicability of SLAM system has become a new hot topic after the accuracy and robustness, e.g., how to keep the stability of the system and achieve accurate pose estimation in the low-texture and dynamic environment, and how to improve the universality and real-time performance of the system in the real scenes, etc. This paper proposes a real-time stereo indirect visual SLAM system, PLD-SLAM, which combines point and line features, and avoid the impact of dynamic objects in highly dynamic environments. We also present a novel global gray similarity (GGS) algorithm to achieve reasonable keyframe selection and efficient loop closure detection (LCD). Benefiting from the GGS, PLD-SLAM can realize real-time accurate pose estimation in most real scenes without pre-training and loading a huge feature dictionary model. To verify the performance of the proposed system, we compare it with existing state-of-the-art (SOTA) methods on the public datasets KITTI, EuRoC MAV, and the indoor stereo datasets provided by us, etc. The experiments show that the PLD-SLAM has better real-time performance while ensuring stability and accuracy in most scenarios. In addition, through the analysis of the experimental results of the GGS, we can find it has excellent performance in the keyframe selection and LCD.