# Literature review — semantic work-zone digital twins for mobile robots

**Review snapshot:** 2026-08-08  
**Scope:** metric 3-D mapping, work-zone perception, semantic fusion,
traversability, and policy-aware operational maps for a robot or quadruped
surveying a road work zone. This review does not claim that any cited system
validates the complete pipeline proposed here.

## Research gap

The literature contains strong components, but not a single validated chain
from a mobile-robot patrol through dense work-zone reconstruction and semantic
scene understanding to an authoritative robot keep-out map. WorkZone3D and
ROADWork establish that work-zone perception is a specialized, long-tailed
problem. LIO-SAM, Voxblox, Kimera, and Hydra provide relevant mapping building
blocks. Traversability studies show that mobility depends on geometry,
uncertainty, and robot capability. Navigation frameworks can enforce keep-out
masks, but they do not decide which regions are legally or operationally closed.

The defensible research question is therefore:

> Can a mobile patrol create an uncertainty-aware metric-semantic model that
> supports three distinct downstream decisions—where the robot may travel,
> what it should inspect, and where it must abstain or remain outside—while
> preserving the separation between perceived evidence and authorized policy?

## Four layers that must not be conflated

| Layer | Example representation | What it can support | What it cannot establish alone |
|---|---|---|---|
| Metric geometry | registered point cloud, mesh, occupancy/TSDF/ESDF | surface shape, obstacle distance, clearance, coverage | object meaning or work-zone authority |
| Observed semantics | road, shoulder, cone, barrel, barrier, worker, equipment, debris, unknown | scene inventory and object-conditioned inspection | legal closure, safe access, or robot capability |
| Derived traversability | slope, roughness, clearance, support, uncertainty, robot envelope | robot-specific mobility estimate | authorization to enter a live or protected area |
| Authoritative policy | approved TTC plan, supervisor-defined corridor, WZDx event/device record, keep-out mask | allowed corridor and enforced no-go regions | proof that perception is correct or terrain is physically traversable |

In the present pilot, operational labels are scored against procedural ground
truth. They are not an approved traffic-control plan. A field deployment must
combine perception with site-authorized constraints and retain an `unknown /
abstain` state.

## Primary-source evidence matrix

| Source | Evidence contributed | Directly relevant limitation |
|---|---|---|
| Sural, Sahu & Rajkumar, **WorkZone3D**, WACV 2026 | A multimodal work-zone dataset with synchronized cameras/lidars and 3-D boxes for cones, barrels, and channelizers; motivates sensor fusion for small devices with few LiDAR returns. | Object detection/localization is not dense robot-centric reconstruction or traversability. Public release and license must be verified before reuse. |
| Ghosh et al., **ROADWork**, ICCV 2025 | Work-zone-specific recognition, sign understanding, instance segmentation, discovery, and video pathway prediction across diverse real imagery. | Primarily 2-D/video navigation evidence; it is not a dense 3-D ground-truth benchmark for a quadruped patrol. |
| Behley et al., **SemanticKITTI**, ICCV 2019 | Dense point-wise semantic labels for sequential LiDAR scans and a standard semantic scene-understanding benchmark. | General road-driving ontology; not work-zone or legged-robot specific. Dataset and underlying KITTI terms require a separate license review before commercial reuse. |
| Shan et al., **LIO-SAM**, IROS 2020 | Factor-graph lidar-inertial odometry, de-skewing, local scan matching, keyframes, and loop-closure integration. | This pilot uses known procedural poses and does not evaluate SLAM or pose drift. |
| Rosinol et al., **Kimera**, ICRA 2020 | Real-time metric-semantic localization, 3-D meshing, and dense semantic reconstruction. | A general mapping system; it does not create work-zone policy labels. |
| Oleynikova et al., **Voxblox**, IROS 2017 | Incremental TSDF/ESDF mapping and distance fields suitable for onboard planning. | Geometry and distance do not by themselves distinguish live lanes, inspection targets, or authorized corridors. |
| Hughes et al., **Hydra**, RSS 2022 | Real-time spatial perception and hierarchical 3-D scene graphs, illustrating how objects, places, and geometry can coexist. | Scene graphs remain observations; policy and site authorization must be supplied separately. |
| Fan et al., **STEP**, RSS 2021 | Uncertainty-aware traversability, tail-risk assessment, and risk-aware planning on wheeled and legged robots. | Demonstrated in extreme terrain, not traffic-controlled work zones. |
| Guan et al., **TNS**, RSS 2022 | Construction-site terrain categories and terrain-traversability mapping/navigation for autonomous excavators. | Robot platform, task, and operational context differ from a compact inspection robot near live traffic. |
| Wetzel et al., **Boston Dynamics Spot for LiDAR scanning**, ISARC 2022 | Field evidence that a quadruped can support repeated LiDAR scanning on active construction sites. | Construction progress scanning is not work-zone semantic safety classification or autonomous lane-policy inference. |
| Tuomisto et al., **quadruped robot + BIM**, Automation in Construction 2026 | Demonstrates integration of quadruped inspection data with building-information context. | Building construction and BIM constraints do not transfer directly to temporary traffic-control geometry. |
| USDOT/FHWA, **WZDx v4.2** | A common event-level data model for work-zone and device information exchange. | WZDx is not centimeter-accurate ground truth and cannot replace local perception or an approved site plan. |
| CARLA, **sensor and semantic-label documentation** | Supports ordinary LiDAR plus depth, RGB, semantic, and instance cameras; defines sensor coordinate conventions and oracle semantic outputs. | Semantic LiDAR is simulator ground truth. It must be used as an annotation/evaluation channel—not presented as a learned perception result. CARLA has no cone-specific semantic class by default. |
| Nav2, **Keepout Filter documentation** | Shows how a navigation stack can enforce a supplied mask. | The filter consumes a mask; it does not infer that the mask is legally or operationally correct. |

## What the literature changes in this study

1. **Use an explicit ontology.** Small work-zone devices need task-specific
   categories, while the operational map uses only `traversable`, `inspect`,
   `no-go`, and `unknown`.
2. **Measure coverage and abstention.** Unknown cells count as misses in mIoU,
   but are preferable to silently labeling an unobserved live lane as safe.
3. **Treat patrol design as part of perception.** Multiple viewpoints and an
   out-and-back return pass are evaluated because device occlusion and sparse
   returns are central failure modes. SLAM loop closure is not evaluated here.
4. **Separate perception from authority.** In field work, approved TTC geometry
   or supervisor-defined constraints must override or gate inferred semantics.
5. **Stage the evidence.** A deterministic procedural fusion pilot comes first;
   a CARLA oracle-pose acquisition study, SLAM/pose-noise benchmark, and controlled
   hardware trial are separate validation gates.

## References

1. S. Sural, N. Sahu, and R. Rajkumar, “WorkZone3D: A Multimodal Dataset for 3D Work Zone Perception in Autonomous Driving,” *WACV*, 2026. <https://openaccess.thecvf.com/content/WACV2026/html/Sural_WorkZone3D_A_Multimodal_Dataset_for_3D_Work_Zone_Perception_in_WACV_2026_paper.html>
2. A. Ghosh et al., “ROADWork: A Dataset and Benchmark for Learning to Recognize, Observe, Analyze and Drive Through Work Zones,” *ICCV*, 2025. <https://openaccess.thecvf.com/content/ICCV2025/html/Ghosh_ROADWork_A_Dataset_and_Benchmark_for_Learning_to_Recognize_Observe_ICCV_2025_paper.html>
3. J. Behley et al., “SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences,” *ICCV*, 2019. <https://openaccess.thecvf.com/content_ICCV_2019/html/Behley_SemanticKITTI_A_Dataset_for_Semantic_Scene_Understanding_of_LiDAR_Sequences_ICCV_2019_paper.html>
4. T. Shan et al., “LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping,” *IROS*, 2020. <https://arxiv.org/abs/2007.00258>
5. A. Rosinol et al., “Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and Mapping,” *ICRA*, 2020. <https://arxiv.org/abs/1910.02490>
6. H. Oleynikova et al., “Voxblox: Incremental 3D Euclidean Signed Distance Fields for On-Board MAV Planning,” *IROS*, 2017. <https://arxiv.org/abs/1611.03631>
7. N. Hughes et al., “Hydra: A Real-time Spatial Perception System for 3D Scene Graph Construction and Optimization,” *RSS*, 2022. <https://www.roboticsproceedings.org/rss18/p050.pdf>
8. D. D. Fan et al., “STEP: Stochastic Traversability Evaluation and Planning for Risk-Aware Off-road Navigation,” *RSS*, 2021. <https://roboticsproceedings.org/rss17/p021.html>
9. T. Guan et al., “TNS: Terrain Traversability Mapping and Navigation System for Autonomous Excavators,” *RSS*, 2022. <https://www.roboticsproceedings.org/rss18/p049.html>
10. J. Wetzel et al., “The Use of Boston Dynamics Spot in Support of LiDAR Scanning on Active Construction Sites,” *ISARC*, 2022. <https://www.iaarc.org/publications/2022_proceedings_of_the_39th_isarc_bogota_colombia/the_use_of_boston_dynamics_spot_in_support_of_lidar_scanning_on_active_construction_sites.html>
11. J. Tuomisto et al., “Quadruped robot and building information modeling integration for construction site data collection,” *Automation in Construction*, 2026, doi:10.1016/j.autcon.2025.106571. <https://doi.org/10.1016/j.autcon.2025.106571>
12. USDOT/FHWA, “Work Zone Data Exchange (WZDx), version 4.2.” <https://www.transportation.gov/av/data/wzdx>
13. CARLA, “Sensors reference” and “Semantic segmentation.” <https://carla.readthedocs.io/en/latest/ref_sensors/>; <https://carla.readthedocs.io/en/latest/tuto_D_create_semantic_tags/>
14. Open Navigation LLC, “Navigating with Keepout Zones.” <https://docs.nav2.org/tutorials/docs/navigation2_with_keepout_filter.html>
