SD-SLAM Semantic Dynamic LiDAR
Last updated: 2026-05-09
Executive Summary
SD-SLAM is a semantic LiDAR SLAM method for dynamic scenes. Its full title is "SD-SLAM: A Semantic SLAM Approach for Dynamic Scenes Based on LiDAR Point Clouds."
The method uses semantic information and Kalman filtering to distinguish dynamic, semi-static, and static landmarks, then uses the more reliable landmark classes to improve localization and build a static semantic map.
What It Is
The arXiv version was submitted in February 2024. The starting source list also gives DOI 10.1016/j.iot.2024.101209. The paper evaluates on the KITTI odometry dataset and frames the problem as semantic SLAM for LiDAR point clouds rather than visual dynamic SLAM.
SD-SLAM is useful as a semantic dynamic-SLAM reference. It is less directly deployment-ready than simple runtime filters unless the semantic segmentation, landmark classification, and map lifecycle policy are validated for the target domain.
Core Technical Idea
Many SLAM systems either remove dynamic objects or ignore semantics. SD-SLAM separates landmarks into dynamic, semi-static, and pure static categories using semantic information and Kalman filtering. It then uses semi-static and static semantic landmarks in the SLAM process instead of discarding all non-static-looking observations.
This distinction matters for real deployments:
| Landmark type | Interpretation | Use risk |
|---|---|---|
| Dynamic | Moving actor or transient observation | Usually reject from map/localization. |
| Semi-static | Parked or movable object with temporary stability | Useful only with caution and temporal policy. |
| Pure static | Persistent infrastructure | Preferred for localization and static map building. |
Inputs and Outputs
| Item | Role |
|---|---|
| LiDAR point clouds | Main geometric observations. |
| Semantic labels | Object or class cues for landmark classification. |
| Kalman filtering | Temporal estimation for dynamic/semi-static distinction. |
| Landmark map | Semantic static and semi-static structure. |
| Odometry/trajectory | SLAM pose output. |
| Static semantic map | Output map with multiple semantic classes. |
Pipeline
- Receive LiDAR point clouds.
- Generate or ingest semantic labels for point-cloud landmarks.
- Track landmark states through time with Kalman filtering.
- Classify landmarks as dynamic, semi-static, or pure static.
- Reject or downweight dynamic landmarks.
- Use semi-static and pure static semantic landmarks for localization and mapping.
- Update the semantic map with stable landmark evidence.
- Publish trajectory and a static semantic map.
Evaluation Snapshot
The arXiv abstract reports tests on the KITTI odometry dataset. It states that SD-SLAM mitigates adverse effects from dynamic objects, improves vehicle localization and mapping in dynamic scenes, and constructs a static semantic map with multiple semantic classes.
For local evaluation, measure trajectory error, map semantic consistency, dynamic/semi-static classification accuracy, localization sensitivity to semantic errors, and static-map contamination by movable objects.
Strengths
- Explicitly models semi-static landmarks instead of treating all object-like observations as dynamic.
- Semantic map output is useful for navigation, QA, and higher-level reasoning.
- Kalman filtering adds temporal consistency to semantic decisions.
- LiDAR-only geometry avoids lighting dependence of camera-only semantic SLAM.
- KITTI evaluation makes it easy to compare with road-scene baselines.
Failure Modes
- Semantic segmentation errors directly affect landmark use.
- Semi-static objects can be dangerous localization anchors in production maps.
- KITTI road classes do not cover airside or warehouse assets.
- No official code was found from the starting sources.
- The DOI/publisher record was less accessible than the arXiv page, so implementation details should be checked against the final article.
Airside/Indoor/Outdoor Fit
| Environment | Fit | Notes |
|---|---|---|
| Airside apron | Research reference | Semantic classes must include aircraft, GSE, buses, crew, cones, chocks, and stand equipment. |
| Indoor warehouses | Research reference | Needs indoor semantic labels for racks, pallets, forklifts, doors, and people. |
| Outdoor roads/campus | Stronger fit | KITTI evaluation aligns with vehicles, roads, and urban structure. |
| Production static maps | Use cautiously | Semi-static landmarks should not be promoted without cross-session evidence. |
Implementation Notes
- Treat semantic labels as uncertain measurements, not ground truth.
- Keep dynamic, semi-static, and static map layers separate.
- Require cross-session policy before promoting semi-static landmarks to a production localization map.
- Evaluate per-class false positives and false negatives before measuring aggregate trajectory error.
- Compare against Semantic SLAM, Dynamic-Object-Aware SLAM, and geometry-only cleaners.
Sources
- arXiv: https://arxiv.org/abs/2402.18318
- arXiv DOI: https://doi.org/10.48550/arXiv.2402.18318
- Journal DOI from starting source: https://doi.org/10.1016/j.iot.2024.101209
- Local context: Semantic SLAM
- Local context: Dynamic-Object-Aware SLAM