PIDNet-SLAM: A Multi-Resolution Semantic SLAM Algorithm for Dynamic Scenes
Bibliographic record
Abstract
Simultaneous Localization and Mapping (SLAM) plays a vital role in the fields of Computer Vision and Robotics. Traditional SLAM frameworks often assume a static environment, which introduces significant errors during localization and mapping. To address this issue, researchers have incorporated various techniques to remove dynamic objects from the scene, but these methods fail to operate at real-time speeds. In this paper, we propose PIDNet-SLAM a semantic segmentation-based SLAM system optimized for dynamic environments. The system enhances the accuracy of ORB-SLAM3 by adding three parallel threads to the architecture: a fast semantic segmentation module using two branches of PIDNet for low and high resolutions separately, and a lightweight geometric module. We employed a novel training method to train the multi resolution PIDNet network, that utilizes the low-resolution branch to process non-keyframes and the high-resolution branch to process keyframes. This approach achieves high inference speeds while maintaining great accuracy. This multi-branch architecture significantly improves the performance of the semantic segmentation network, making it highly suitable for real-time applications. We evaluated our SLAM system on the TUM RGB-D dataset, comparing it with ORB-SLAM3, Dyna SLAM, and SOLO-SLAM. Our system achieved significant improvements, including enhanced localization accuracy in highly dynamic environments and an average processing speed of 47ms outperforming all other SLAM algorithms in handling dynamic scenes while operating in real-time.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".