KORTHOS / ECOSYSTEM / PRODUCT

ZED Mini

About

About ZED Mini

ZED Mini uses two synchronised cameras separated by a 6.3 cm baseline to calculate depth through stereo correspondence. Video modes extend from 2K at 15 frames per second to 720p at 60 frames per second, while the built-in six-degree-of-freedom IMU supports camera-motion tracking. The compact body is suited to head-mounted, desktop and smaller robotic installations.

The USB camera streams stereo imagery to a host NVIDIA GPU running the ZED SDK, which produces depth maps, positional tracking, spatial mapping and object-detection inputs. Developers mount it on a robot, headset or test rig and combine the output with their own navigation or spatial application. ZED Mini remains available as an established compact member of Stereolabs’ USB camera range.

Research

  • Low-Cost Stereo Vision for Robust 3D Positioning of Thin Radiata Pine Branches in Autonomous Drone Pruning

    2026-05-06 - Research Publication - The proposed pipeline comprises two stages: branch segmentation and depth estimation.

    Source
  • Positioning radiata pine branches requiring pruning by drone stereo vision

    2026-04-12 - Research Publication - This paper presents a stereo-vision-based system mounted on a drone for detecting and localising radiata pine branches to support autonomous pruning.

    Source
  • Real-Time Branch-to-Tool Distance Estimation for Autonomous UAV Pruning: Benchmarking Five DEFOM-Stereo Variants from Simulation to Jetson Deployment

    2026-03-27 - Research Publication - Autonomous tree pruning with unmanned aerial vehicles (UAVs) is a safety-critical real-world task: the onboard perception system must estimate the metric distance from a cutting tool to thin tree branches in real time so that the UAV can approach, align, and actuate the pruner wi

    Source
  • UE5-Forest: A Photorealistic Synthetic Stereo Dataset for UAV Forestry Depth Estimation

    2026-03-13 - Research Publication - Dense ground-truth disparity maps are practically unobtainable in forestry environments, where thin overlapping branches and complex canopy geometry defeat conventional depth sensors -- a critical bottleneck for training supervised stereo matching networks for autonomous UAV-base

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  • Canterbury tree-branches dataset paper uses ZED Mini for forestry perception data

    2026-02-24 - Research Publication - This dataset covers Canterbury tree-branches dataset paper uses ZED Mini for forestry perception data for ZED Mini in Navigation.

    Source
  • Training Deep Stereo Matching Networks on Tree Branch Imagery: A Benchmark Study for Real-Time UAV Forestry Applications

    2026-02-23 - Research Publication - The paper proposes balanced active perception for finding target objects in clutter using sensor motion planning. The robotic perception setup uses a ZED Mini camera as part of the sensing system.

    Source
  • Generalization Evaluation of Deep Stereo Matching Methods for UAV-Based Forestry Applications

    2025-12-03 - Research Publication - The authors present the first systematic zero-shot evaluation of eight state-of-the-art stereo methods--RAFT-Stereo, IGEV, IGEV++, BridgeDepth, StereoAnywhere, DEFOM (plus baseline methods ACVNet, PSMNet, TCstereo)--spanning iterative refinement, foundation model, and zero-shot adaptation paradigms.

    Source
  • Brain over Brawn: Using a Stereo Camera to Detect, Track, and Intercept a Faster UAV by Reconstructing the Intruder's Trajectory

    2021-07-02 - Research Publication - This paper presents their approach to intercepting a faster intruder UAV, inspired by the MBZIRC 2020 Challenge 1.

    Source

Context

Market context

ZED Mini is a compact Stereolabs stereo camera for AR, VR and lightweight robotics applications that need depth sensing and tracking.

  • 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 Mini 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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