DR-REMOVER
Last updated: 2026-05-09
Executive Summary
DR-REMOVER is an offline dynamic-object remover for static 3D point-cloud map construction. Its full title is "DR-REMOVER: An Efficient Dynamic Object Remover Using Dual-Resolution Occupancy Grids for Constructing Static Point Cloud Maps."
The key idea is dual resolution: coarse grids quickly find regions likely to contain dynamic objects, and fine grids verify dynamic points while reverting static points that were removed too aggressively.
What It Is
The paper appears in IEEE Transactions on Intelligent Vehicles, volume 9, issue 12, pages 8027-8039, 2024. Bibliographic records list the DOI as 10.1109/TIV.2024.3406334.
DR-REMOVER is closer to ERASOR, Removert, and LiDAR map cleaning than to online LIO front ends. It assumes an accumulated mapping pipeline and focuses on producing a cleaner static map.
Core Technical Idea
Dynamic objects create inconsistent occupancy in an accumulated map. DR-REMOVER first uses low-resolution occupancy grids to identify candidate regions with dynamic-object traces. It then uses high-resolution grids to verify individual dynamic points and restore static points that should not be deleted.
This two-level strategy is designed to balance speed and preservation:
| Resolution | Main role | Practical effect |
|---|---|---|
| Low resolution | Find dynamic-object candidate regions | Reduces search cost and handles sparse point clouds. |
| High resolution | Verify and revert point decisions | Improves static preservation near dynamic objects. |
Inputs and Outputs
| Item | Role |
|---|---|
| Registered LiDAR scans | Source observations for occupancy comparison. |
| Estimated poses | Needed to accumulate scans into a shared map frame. |
| Low-resolution grid | Candidate dynamic-region detection. |
| High-resolution grid | Point-level verification and static reversion. |
| Static/dynamic labels | Map-point decisions for QA and export. |
| Clean static map | Main output for localization, planning, or annotation. |
Pipeline
- Build or load an accumulated point-cloud map with scan poses.
- Encode map observations into dual-resolution occupancy grids.
- Use the low-resolution grid to locate bins likely to contain dynamic traces.
- Refine candidate bins using a high-resolution grid.
- Verify dynamic points while considering nearby environment structure.
- Revert false removals where high-resolution evidence supports static structure.
- Export static and removed-point layers for map QA.
- Evaluate preservation rate, rejection rate, and localization impact.
Evaluation Snapshot
The TRID record reports evaluation on SemanticKITTI, Apollo, and an unmanned ground vehicle dataset with highly crowded environments. It reports more than 95% preservation rate for static points and rejection rate for dynamic points on all experimental sequences.
For local validation, reproduce PR/RR and add production metrics: localization residuals on cleaned maps, false removal by infrastructure class, ghost rate per route segment, and cleaner disagreement against ERASOR, Removert, MapCleaner, and FreeDOM.
Strengths
- Focused on static map construction, which is directly useful for AV map publication.
- Dual-resolution design targets both efficiency and preservation.
- Reversion step reduces the risk of deleting real static structure.
- Reported evaluation includes SemanticKITTI, Apollo, and crowded UGV data.
- Class-agnostic geometry can cover actors not present in semantic training sets.
Failure Modes
- Output quality depends on pose quality; bad registration creates false occupancy disagreement.
- Temporarily parked objects can still look static if observations are insufficient.
- Thin poles, signs, curbs, aircraft gear, and sparse long-range points can be over-removed.
- Offline operation does not protect real-time odometry unless paired with an online filter.
- The IEEE article appears closed access, so implementation details need full-paper access or reproduction.
Airside/Indoor/Outdoor Fit
| Environment | Fit | Notes |
|---|---|---|
| Airside static maps | Strong candidate | Good fit for removing GSE, buses, aircraft traces, and crowd artifacts from survey maps. |
| Indoor warehouses | Moderate | Useful for forklift/person traces, but grid resolution must preserve racks, posts, and loading-dock edges. |
| Outdoor roads/campus | Strong | Aligns with SemanticKITTI/Apollo-style static map cleaning. |
| Runtime LIO | Limited | Use as offline map cleaning; it is not a real-time odometry front end by itself. |
Implementation Notes
- Treat DR-REMOVER as a map-build stage after trajectory optimization.
- Preserve both static output and removed dynamic layers for manual QA.
- Tune grid resolution per sensor, route scale, and expected thin infrastructure.
- Test localization on both raw and cleaned maps before accepting a cleaner configuration.
- Use quiet-survey passes to measure false removal of static apron assets.
Sources
- DOI: https://doi.org/10.1109/TIV.2024.3406334
- TRID record: https://trid.trb.org/View/2591781
- dblp record: https://dblp.org/rec/journals/tiv/ZhangZWH24
- Local context: LiDAR Map Cleaning and Dynamic Removal
- Local baselines: ERASOR, Removert