KORTHOS / ECOSYSTEM / PRODUCT

ZED X

About

About ZED X

ZED X captures synchronised 1920 × 1200 global-shutter images from two lenses at up to 60 frames per second. The stereo pair feeds the ZED SDK, which calculates dense depth, visual odometry, spatial mapping and object detections on an NVIDIA Jetson or other supported host.

GMSL2 carries data and power over long shielded cables with lower latency and stronger electromagnetic immunity than a typical USB connection. An IP67 enclosure supports outdoor and industrial operation, while multi-camera synchronisation allows several ZED X units to provide wider coverage around a robot or vehicle.

Research

  • UGV_environment_recordings_metro_madrid_Dec_25

    2026-01-16 - Research Publication - Ros2 bags with RGB streams from ZED X cameras mounted on UGV. UGV LEDs were turned on during the recording. Info from 3 cameras (front, back and poiting towards wall). In the left camera pointing at the wall you can see km points in some recordings. The front and back cameras point at workers. To consume the streams in ROS2 Humble, follow instructions underneath. ROS 2 Humble — Play a Zenoh-recorded rosbag 0) Extract rosbags (each rosbag is a directory, not a single file) 1) Install zenoh (only once) sudo apt update sudo apt install -y ros-humble-rmw-zenoh-cpp 2) Terminal 1 — Zenoh router + daemon reset (ONCE) # This terminal defines the RMW used by the ROS 2 CLI. source /opt/ros/humble/setup.bash export RMW_IMPLEMENTATION=rmw_zenoh_cpp # Reset ROS 2 CLI daemon ONCE ros2 daemon stop ros2 daemon start # Start Zenoh router ros2 run rmw_zenoh_cpp rmw_zenohd Leave this terminal running. 3) T

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  • Depth Estimation Comparison Between Realsense D435i, D435f, Stereolabs ZED X and ZED X Mini

    2026-01-01 - Research Publication - RGB-D cameras are essential in robotics, offering a cost-effective means of achieving depth perception for a variety of tasks, including planning, navigation and manipulation.

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  • Semi-Autonom Dockning av Flygplansdragare med hjälp av en Stereokamera

    2026-01-01 - Research Publication - Towbarless aircraft tugs dock by lifting the aircraft by placing the airplanes nose gear in acradle and lifting it up, handing over the control to the tug. This docking procedure requiresa skilled driver, and driver assistance functions are desired to improve the efficiency. Thisthesis investigates the development of a semi-autonomous driver assistance system for thedocking procedure.The proposed perception and navigation pipeline utilizes a ZED X stereo camera withina ROS2 framework. A custom-trained YOLO model is employed to detect and localize thenose gear, while an Iterative Closest Point (ICP) algorithm, combined with bounding boxdata, estimates the yaw angle from the 3D point cloud. For autonomous vehicle control, apath-following navigation system based on Dubins paths and a Pure Pursuit algorithm wasimplemented. The system relies on the camera’s built-in VSLAM for odometry and tra

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  • Realtids flerkamera-datorseende för närhetsmedveten mjukstopp och nödbromssystem i autonoma lantbruksfordon

    2026-01-01 - Research Publication - Autonomous agricultural vehicles are increasingly being deployed for tasks such as tilling, planting, and harvesting as well as for continuous crop monitoring. One such platform is the Drever 120, developed by TraktorArvid AB. Currently, the vehicle's safety architecture relies on a front-mounted laser sensor for initial deceleration and pressure-sensitive bumpers to detect physical collisions. While this contact-based system ensures fundamental machine safety, triggering these physical sensors forces the tractor into a hard emergency stop that completely shuts down autonomous operations. Because this binary safety response requires a human operator to physically travel to the vehicle, inspect the hazard, and manually restart the system, it creates significant operational downtime during time-critical farming windows. To address this limitation, this thesis proposes and evaluates a real-

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  • SEEmear: High-Resolution Georeferenced Proximal RGB-D Data for Niagara Grapes in the Field

    2025-12-17 - Research Publication - This dataset contains high-resolution geo-referenced RGB-D data collected in vineyards using the SEEmear proximal sensing system. The data were acquired at farms in São Miguel Arcanjo, São Paulo, Brazil, during field tests conducted in December 2025. Data Collection System The SEEmear integrates stereo cameras (ZED X, Stereolabs Inc.) with high-resolution global shutter sensors (1920 × 1200 pixels), wide-angle lenses, and integrated IMUs, mounted on a mobile platform. The system includes RTK-GNSS positioning (Reach M2 rover and Reach RS3 base station) for centimeter-level accuracy, enabling precise georeferencing of all captured data. Dataset Contents RGB-D stereo images at 30 fps IMU measurements synchronized with camera frames RTK GNSS positioning data GPS time synchronization for temporal consistency Applications This dataset supports research in precision agriculture, plant phenotypi

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  • Replication data for "Depth Estimation Comparison Between Realsense D435i, D435f, Stereolabs ZED X and ZED X Mini"

    2025-11-18 - Research Publication - An open ROS 2 dataset and analysis codebase for comparing depth accuracy across RealSense D435i/D435f and ZED X/ZED X Mini cameras.

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  • SEEmear: high-resolution georeferenced RGB-D data from apple orchard under palmette training system

    2025-03-13 - Research Publication - This dataset contains high-resolution georeferenced RGB-D data collected from apple orchards using the SEEmear proximal sensing system. The data was acquired at Embrapa's Temperate Climate Fruit Growing Experimental Station (EFCT) in Vacaria, Rio Grande do Sul, Brazil, during field tests conducted in March 2025. Data Collection System: SEEmear integrates stereo cameras (ZED X, Stereolabs Inc.) with high-resolution global-shutter sensors (1920 × 1200 pixels), wide-angle lenses, and integrated IMUs, mounted on a mobile platform. The system includes RTK-GNSS positioning (Reach M2 rover and Reach RS3 base station) for centimeter-level accuracy, enabling precise georeferencing of all captured data. Dataset Contents: RGB-D stereo imagery at 30 fps IMU measurements synchronized with camera frames GNSS RTK positioning data GPS time synchronization for temporal consistency Orchard Characteristics

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Timeline

  • AVerMedia and Stereolabs integrate JetPack 7.2 support for ZED X vision

    2026-10-07 - Capability - AVerMedia handles board-specific device trees, driver generation and validation, while Stereolabs maintains camera firmware, image processing and the ZED SDK. Reproducible Debian packages retain the driver, device tree, installation flow and kernel code used in production. Drivers can be regenerated for required JetPack and Linux versions; JetPack 7.2 also adds Yocto support for reproducible software images.

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  • Stereolabs launches ZED X One camera series for Physical AI

    2025-12-09 - Product Launch - Stereolabs launched the ZED X One S and ZED X One Core as new cameras for physical AI and data-collection workflows. The company said the series was designed for constrained and rugged deployments including humanoids, robotic arms, and factory automation environments. The launch broadened the ZED family toward compact monocular perception modules aimed at large-scale robotics and training deployments.

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  • Stereolabs integrates ZED SDK and ZED X with NVIDIA Isaac Sim and Isaac ROS

    2023-10-17 - Partnership - Stereolabs integrated the ZED SDK and ZED X stereo cameras with NVIDIA Isaac Sim and added support for NVIDIA Isaac ROS. The company positioned the integration around simulation-first robot development, synthetic training workflows, and streamlined ROS deployment for space-aware autonomous robots. The release extended Stereolabs from stereo vision hardware into a deeper robotics development-stack partnership with NVIDIA.

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Context

Market context

Next-generation stereo camera designed for robotics and autonomous systems.

  • Target environment: Robotics, automation and edge-AI systems that need sensing, depth perception, vision processing or local compute.
  • Workflow context: Depth sensing, visual perception, edge processing, object detection, tracking and robot awareness.
  • Customer context: Robot builders, machine-vision teams, automation engineers and edge-AI developers.
  • Deployment model: Commercial or available product positioned for customer use, integration or developer adoption.
  • Commercial maturity: Publicly presented as commercial or available, with maturity depending on deployment scale and support coverage.
  • Adoption constraints: Adoption depends on accuracy, latency, environmental robustness, SDK quality, compute budget and integration with the robot stack.
  • Market position: ZED X sits in the perception and compute layer that helps robots understand or process the physical world.
  • Adjacent products: ZED cameras, Ouster lidar, Cognex VisionPro, NVIDIA Jetson

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