Dynamic SLAM: A Visual SLAM in Outdoor Dynamic Scenes
Bibliographic record
Abstract
Simultaneous localization and mapping (SLAM) has been widely used in augmented reality (AR), virtual reality (VR), robotics, and autonomous vehicles as the theoretical basis for robots to perceive their environment. Most popular SLAM algorithms assume that objects in the scene are static. Solving dynamic problems in SLAM is now attracting increasing attention. In this paper, we propose a method that combines semantic segmentation information and spatial motion information of associated pixels to cope with dynamic objects based on ORB-SLAM2. We add a deep segmentation network SegNet to segment input image and obtain the semantic information for each feature point. Next, the spatial velocity of feature points between adjacent frames is calculated assuming uniform motion. Finally, the two parts are fused for the final judgment, and the dynamic feature points are removed to improve positioning accuracy. We evaluate our SLAM algorithms using the public KITTI dataset. The proposed algorithm has a similar overall accuracy level to ORB-SLAM2, but it is more accurate in sequences with many dynamic objects. On KITTI’s raw data sequence containing multiple dynamic objects, our pipeline achieves the best performance, improving 39.5% compared with the original ORB-SLAM2 system. We compare our algorithm with other state-of-the-art SLAM systems used to cope with dynamic environments. The results show that the proposed algorithm has better performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".