CD-SLAM: A Real-Time Stereo Visual–Inertial SLAM for Complex Dynamic Environments With Semantic and Geometric Information
Bibliographic record
Abstract
The most commonly used simultaneous localization and mapping (SLAM) scheme often assumes a static environment, leading to significant errors in pose estimation when operating in highly dynamic scenes. To address this limitation and improve the robustness and accuracy of positioning in dynamic environments, this study proposes CD-SLAM, a real-time stereo vision inertial SLAM system specifically designed for complex dynamic environments, based on ORB-SLAM3. CD-SLAM enhances the tracking thread and introduces a new parallel thread that utilizes YOLOv5 to detect objects in each input frame and extract semantic information. This semantic information, combined with prior information from the inertial measurement unit (IMU), is used for pose estimation, eliminating the pose information of dynamic objects and consequently improving the accuracy and robustness of positioning. Furthermore, CD-SLAM employs scene flow to calculate the distance between adjacent frames and determine the spatial velocity between them, compensating for potential static information through a velocity filtering algorithm. To enhance positioning accuracy in challenging environments with weak textures, CD-SLAM integrates an IMU for motion prediction and coherence detection. Finally, appeal information is integrated to determine the motion status of objects in the scene and filter out dynamic feature points. Experimental tests conducted on the VIODE dataset demonstrate that CD-SLAM outperforms existing algorithms in terms of accuracy and robustness.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".