Related references
Note: Only part of the references are listed.
Article
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Summary: The paper presents a robust and real-time RGB-D SLAM algorithm based on ORBSLAM3. It addresses the challenges faced in feature point matching-based SLAM in complex and changeable indoor environments. The proposed algorithm improves pose estimation accuracy in the presence of dynamic objects and avoids tracking loss in weak texture scenes. Experimental results show that the algorithm achieves significantly improved performance compared to existing algorithms in terms of accuracy and real-time capability.
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Article
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Article
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Summary: This study proposes a point-line SLAM system based on dynamic environments. It obtains dynamic region features through detection and segmentation of dynamic regions. The separation of dynamic and static objects is achieved through a geometric constraint method for matching line segments and a dynamic feature tracking method based on Bayesian theory, improving the robustness and accuracy of the SLAM system.
Article
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Summary: SLAM technology is used for locating and mapping in unknown environments, but the constructed maps often lack readability and interactivity, making it difficult to grasp the information accurately. To enable intelligent robots to interact meaningfully with their environment, it is necessary to understand both the geometric and semantic properties of the scene. The proposed method reduces absolute positional errors, constructs dense semantic point cloud maps, and segments point cloud models of objects in the environment with high accuracy.
Proceedings Paper
Computer Science, Artificial Intelligence
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Summary: This method improves the performance of multi-object tracking by associating almost every detection box, effectively solving the problem of true object missing and fragmented trajectories caused by low score detection boxes being discarded. Applied to multiple trackers, this method consistently achieves improvement on IDF1 score.
COMPUTER VISION, ECCV 2022, PT XXII
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Summary: The proposed switching-coupled back-end solution offers a flexible approach to address the limitations in SLOT problem. By considering the uncertainty, observation quality, and prior information of objects, dynamic objects' states can be coupled with camera and static landmarks' states, leading to effective object tracking processes. Extensive evaluations on synthetic scenes, KITTI datasets, and real-world experiments demonstrate the performance of the method.
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Proceedings Paper
Automation & Control Systems
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