Hudhud deployed CHCNAV’s AU20 mobile mapping system (MMS) to survey tens of thousands of kilometres of road in Riyadh, Saudi Arabia, as part of building a high‑resolution digital basemap of the city. The resulting dataset now supports Hudhud’s navigation application on iOS and Android and provides a scalable base for continuous updates.
Saudi geospatial technology company Hudhud launched an initiative to build a high‑resolution digital basemap of Riyadh to deliver locally optimized geospatial intelligence. To accelerate delivery, the company partnered with CHC Navigation (CHCNAV) and deployed the supplier’s AU20 mobile mapping system (MMS) to survey more than 23,000 linear kilometres of the road network in the metropolitan area. The aim was to create a reliable mapping foundation for lane‑level navigation while preparing for broader digital twin applications in smart mobility and smart city services.
Why high-definition data mattered
In complex cities, standard‑definition maps often fall short on positional accuracy, semantic detail and update frequency. Hudhud wanted to develop a proprietary, high‑definition digital twin of Riyadh to reduce reliance on external providers and enable multiple use cases, including:
- Personal navigation: lane‑level guidance, more accurate turns and improved point‑of‑interest positioning
- ADAS/AD readiness: machine‑readable road features such as lane markings, kerbs and traffic signage
- Logistics and commercial use: better road topology and street‑level context to improve last‑mile efficiency and reduce delays
- Smart city operations: potential asset inventory and maintenance planning based on mapped infrastructure
- Urban planning: as‑built documentation aligned with real‑world conditions to support planning and engineering decisions
Achieving these outcomes required high‑resolution, consistent data captured efficiently at city scale without sacrificing accuracy.
Technical challenges: environmental and operational constraints
Riyadh’s high‑rise districts create urban canyons where GNSS signals are blocked or reflected, introducing multipath errors and trajectory drift that can degrade map usability and drive resurveys. Sustained high temperatures and intense sunlight also demand hardware that maintains thermal stability, sensor calibration and uptime over long collection runs. Hudhud’s production pipeline added strict quality requirements. It needed dense point clouds to capture fine surface details and small objects, high‑quality imagery suitable for automated and manual feature extraction under strong sunlight and contrast changes, and continuous trajectories through tunnels, underpasses and bridges to avoid fragmented geometry. Lastly, the project required stable daily operations: limited downtime, predictable calibration behaviour and repeatable outputs to keep the stringent collection schedule on track. After evaluating options, Hudhud selected the CHCNAV AU20 MMS for its integrated sensor architecture and reliability in large‑scale capture.
The solution: CHCNAV AU20 MMS
Hudhud deployed the CHCNAV AU20 MMS on the vehicle platform as an integrated system for 3D geospatial acquisition across urban and highway environments. This delivered the following benefits:
High-density Lidar capture
The AU20 integrates dual Lidar scanners with a combined pulse frequency of up to four million points per second. This supported detailed capture of lane markings and pavement texture at driving speeds, while calibrated tilt angles reduced blind spots and maximized coverage. High point density also increased the likelihood that a single pass was sufficient across diverse road geometries, which was critical when surveying tens of thousands of kilometres.
Real-time waveform processing for challenging surfaces
Real‑time waveform processing (RWP) enhanced detection of low‑reflectivity materials such as asphalt and dark vehicle surfaces. With approximately 5mm resolution, the AU20 helped preserve subtle surface attributes, including painted line thickness, to enrich high‑definition map layers and support additional analytics such as road condition monitoring.
Multi-sensor fusion: imagery, positioning and odometry
The CHCNAV AP7 platform synchronized sensors to produce time‑aligned datasets. A 72MP Ladybug panoramic camera captured 360° street‑view imagery, supported by two 12MP pavement cameras for road surface inspection. For positioning, a tightly coupled GNSS + IMU solution maintained accuracy in GNSS‑challenged areas. A DMI wheel encoder provided speed and distance constraints to reduce drift and keep the trajectory continuous through difficult segments, while the IMU could help maintain trajectory integrity in the case of any satellite outages.
Software workflow: CoPre pre-processing for production efficiency
City‑scale mobile mapping can generate terabytes of raw data per day, so converting acquisition into mapping‑ready outputs is a major determinant of time-to-delivery. CHCNAV’s CoPre pre‑processing suite streamlined Hudhud’s data handling through automation and AI‑assisted workflows:
- Automated object filtration: detection of dynamic elements such as moving vehicles and pedestrians to create cleaner static point clouds and reduce manual cleaning.
- Panoramic colourization: AI‑assisted alignment of 360° imagery with Lidar, applying RGB values to 3D geometry with reported accuracy exceeding 95%. The coloured point cloud improved readability for human operators and supported automated feature extraction for markings, signage and roadside assets
With these steps completed, Hudhud’s internal team focused on feature extraction and final map production using the cleaned, aligned datasets.
Implementation and operational execution
Execution relied on close collaboration between Hudhud’s field teams and CHCNAV’s research and development organization. The AU20 maintained mechanical stability and calibration accuracy during extended acquisition runs despite rough roads and heat, enabling continuous operations aligned with project timelines. CHCNAV also provided tailored deployment support, including cloud‑based processing tools optimized for Hudhud’s workflow and smoother integration with its GIS and map production systems.
Project outcomes and market impact
The AU20 deployment enabled Hudhud to meet technical and commercial objectives while minimizing rework:
- Total coverage: more than 23,000km of road network mapped across the Riyadh metropolitan area
- Data acceptance: processed datasets achieved a 99.99% acceptance rate under Hudhud’s quality control protocols
- Operational efficiency: a near‑zero resurvey rate, with high point density and robust trajectories supporting single‑pass collection in most areas
Overall, the project shows how industrial‑grade mobile mapping hardware combined with efficient processing workflows can scale city‑wide, high-definition (HD) mapping. By leveraging CHCNAV’s AU20 MMS and CoPre automation, Hudhud accelerated delivery of locally relevant, high‑precision geospatial data and avoided the cost and time of building mapping hardware internally. The HD dataset strengthens Hudhud’s consumer navigation experience today and provides a platform for future services in smart mobility, autonomous driving, infrastructure inventory and city operations.