Every robotics project that depends on 3D perception starts with the same high-stakes questions: How many cameras are required? Where should they be mounted? Traditionally, answering these questions has meant procuring hardware, building test rigs, and iterating slowly. This can consume weeks of engineering effort before a single line of application code is proven.
At e-con Systems, we believe the process should be faster, safer, and far more predictable. That’s why we have integrated DepthVista Helix, our Indirect Time-of-Flight depth camera, directly into NVIDIA Isaac Sim, the world’s leading robotics simulator.
It offers a single environment for system integration, algorithm testing, synthetic data generation, and reinforcement learning, with zero hardware risk. This puts a true-to-spec digital twin of the camera, along with its complete ROS 2 output, at your fingertips, ensuring a seamless transition from simulation to real-world deployment.
In this blog, you’ll learn how this integration works, the two reference applications included, the camera capabilities, and how teams can overcome development challenges.
Why Use NVIDIA Isaac Sim for Robotics Camera Development
NVIDIA Isaac Sim is the industry benchmark for robotics simulation. It combines RTX-accurate rendering, a real physics engine, and native ROS 2 support in a single environment where robots move, sense, and interact exactly as they would in the field.
The camera configuration, topics, transforms, and data formats produced in simulation match those generated on real hardware. This enables genuine sim-to-real continuity and empowers your team to develop with confidence long before the physical camera arrives.
Just as importantly, Isaac Sim is far more than a test bed. It is a complete data and training platform: its cameras and depth sensors generate perfectly labeled ground-truth data.
What e-con Systems’ Fully Integrated iToF Camera Delivers
We did not simply drop a 3D model into the simulator. We built a fully integrated NVIDIA Isaac Sim sensor based on the DepthVista Helix, e-con Systems’ Indirect Time-of-Flight (iToF) depth camera. It is ideal for demanding robotics and industrial deployments.
Key specifications
- Depth modes: High-resolution (1280 × 960, up to 2 m) and long-range (640 × 480, up to 6 m), selectable per application
- Frame rate: Up to 60 fps (high-res) / 50 fps (VGA)
- Field of view: 62° × 49° × 83° (H × V × D)
- Motion sensing: Integrated 6-axis IMU
- Connectivity: GMSL or USB variants
- Build: IP67-rated, −10 °C to 55 °C operating range; 95 × 39.5 × 37.5 mm enclosure, 180 g
Ready-to-Run Depth Camera Applications in NVIDIA Isaac Sim
e-con Systems offers two reference scenarios that mirror the most common depth-camera deployments:
- Robotic manipulation: A UR10 arm equipped with both an eye-in-hand and an eye-to-hand DepthVista Helix camera, representing pick-and-place, bin-picking, and inspection workflows
- Autonomous mobile navigation: A four-camera surround configuration on an autonomous mobile robot, feeding NVIDIA’s Nav2 stack for 360° obstacle avoidance across compact and full-scale warehouse environments.
Both scenarios are fully runnable and editable, so you get a proven starting point instead of a blank page.
Beyond Integration: What the DepthVista Helix- NVIDIA Isaac Sim Camera Unlocks
- One-click insertion: Add the DepthVista Helix iToF from the Isaac Sim Create menu, positioned at true scale and ready to mount on your robot.
- Automatic ROS 2 streaming: Use a single script to detect every camera in the scene and publish depth, point clouds, camera calibration, transforms (TF), and 6-axis IMU data, with no manual wiring.
- Instant visualization: Leverage a built-in, browser-based depth viewer that renders live depth and point clouds, with no extra tooling.
- Both connector variants: Ensure a faithful reflection of the real DepthVista Helix product line. This means going from an empty scene to production-shaped depth data on ROS 2 takes just minutes.
How DepthVista Helix Supports Robotics Simulation and Sensor Validation
True-to-hardware optics and geometry
The simulated DepthVista Helix reproduces the real module’s 62° × 49° × 83° (H × V × D) field of view and pixel-perfect image geometry. This helps with checking coverage, detecting blind spots, and generating synthetic training data with domain randomization to close the sim-to-real gap.
Coverage analysis and multi-camera placement decisions reflect the performance of the physical camera rather than an idealized placeholder.
Two selectable depth modes
It is important to use high-res mode (detail up to 2 m) for grasping and inspection tasks, and long-range mode (reach up to 6 m) for obstacle avoidance and path planning. This matches the operating envelope of the physical sensor.
IMU-driven motion sensing
The simulated camera also streams data from its integrated 6-axis IMU, providing inertial measurements alongside depth. It is useful for sensor fusion, motion compensation, and pose estimation without needing a separate simulated IMU rig. For example, a docking or manipulation task can fuse IMU angular rate and acceleration with the depth stream to compensate for camera jitter during motion, improving point cloud stability before it ever reaches the physical robot.
Every depth frame, point cloud, and IMU reading it produces flows directly into Isaac Sim’s perception and robot-learning pipeline. So what you design in simulation reflects the camera you’ll deploy.
Overcome Robotics’ Challenges with DepthVista Helix Simulation
- De-risk camera selection and placement: Using the camera’s true field of view and operating range, evaluate coverage, mounting height, and camera count virtually and expose blind spots before committing to hardware.
- Validate mechanical fit and payload: The camera is modeled at true scale — a 95 × 39.5 × 37.5 mm enclosure at 180 g. So, mounting clearance, reach, and robot-arm payload budgets can be verified before a single bracket is fabricated.
- Choose the right connectivity: Both GMSL and USB variants are provided. Hence, evaluate GMSL for long-cable, multi-camera robots and USB for simpler single-camera setups, and settle your wiring architecture in simulation.
- Accelerate integration: Bring correct transforms, calibration, and the complete ROS 2 topic set online in a single, repeatable step.
- Preserve sim-to-real parity: Because the simulated modes and resolutions match the real module, a single codebase carries from simulation through to the deployed camera unchanged.
- Validate and train faster: Tune navigation and manipulation algorithms, generate labeled datasets, and train RL policies in reproducible simulation environments. Create synthetic datasets that match the real camera’s resolution and field of view, improve sim-to-real robustness through domain randomization, and train reinforcement learning policies using depth and point-cloud data that match the physical camera.
What once required weeks of physical experimentation now takes just an afternoon!
From NVIDIA Isaac Sim to the Physical DepthVista Helix Camera
Ready to move from simulation to deployment? Visit the DepthVista Helix product page to learn about the physical camera.
Stay tuned for our upcoming blogs to see this iToF camera in action for rover navigation!
Get Started Today
With e-con Systems’ DepthVista Helix iToF available in NVIDIA Isaac Sim, you can design your vision system, generate training data, validate your algorithms, and even train reinforcement-learning policies, all before the hardware arrives. It is a faster, lower-risk path from concept to deployment, backed by e-con Systems’ unparalleled experience of having designed, developed, and manufactured embedded vision solutions, from OEM cameras to complete ODM platforms.
Want to learn more about other Isaac Sim–supported e-con cameras, or have a specific requirement?
Fill out this access form to get the GitHub package link. Download it and try ready-to-use, easily testable scenes to get started right away.
You can visit our Camera Selector Page to explore e-con Systems’ complete portfolio. If you have any questions or feedback, please write to camerasolutions@e-consystems.com.
FAQs
What does e-con Systems’ DepthVista Helix integration with NVIDIA Isaac Sim include?
The integration includes one-click camera insertion, automatic ROS 2 streaming, live depth and point-cloud visualization, a 6-axis IMU data stream, and support for both GMSL and USB variants.
Which robotics applications are included as reference scenarios?
The package includes a robotic manipulation scenario using a UR10 arm and an autonomous mobile navigation scenario with four-camera surround coverage for 360° obstacle avoidance.
Which depth modes are available in the simulated camera?
The simulated DepthVista Helix supports a high-resolution mode of 1280 × 960 for distances up to 2 m and a long-range mode of 640 × 480 for distances up to 6 m.
How does the integration support sim-to-real development?
The simulated camera reproduces the physical camera’s field of view, image geometry, depth modes, resolutions, calibration, transforms, and ROS 2 topics, so the same codebase can move from simulation to deployment.
Where can developers access the DepthVista Helix Isaac Sim package?
Developers can fill out this access form. After submission, the webpage redirects them to the GitHub repository containing the package and ready-to-use scenes.
Arun is a seasoned Embedded Vision leader with 11+ years of experience driving innovation in robotics and AI-powered imaging systems. He focuses on developing perception stacks that power intelligent robotic systems, bridging hardware, algorithms, and edge deployment.