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Labforge introduces Snow Flea to the Secretary of State for Defence Procurement.

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Labforge introduces Snow Flea to the Secretary of State for Defence Procurement..

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[av_dropcap1]Waterloo, ON, February 28, 2026 [/av_dropcap1] Stephen Fuhr, the Secretary of State for Defence Procurement, met with a select group of local companies operating within the defence sector. During this meeting, Labforge presented Snow Flea, a distributed sensing network of micro-pods and towers designed for the Arctic protection.

Labforge has been working with DND at various levels for almost 10 years. Snow Flea fuses imaging, radar, depth, acoustics, seismic, and RF sensing into unified situational awareness for civilian and defence use. Our connected micro-pods and towers can be used for protecting vital infrastructure, ports, and early warning sites – together with environmental monitoring, tracking endangered species, and search & rescue. Read the full article.
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March 2, 2026
Labforge Welcomed onto the AI Source List.

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Labforge Welcomed onto the AI Source List.

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[av_dropcap1]Waterloo, ON, March 2, 2026 [/av_dropcap1] Labforge received its qualification from the Government of Canada to the AI Source List. This implies that Labforge is now eligible to participate in subsequent opportunities solicited under this Source List.
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March 2, 2026
Snow Flea featured in The Record.

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Snow Flea featured in The Record.

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[av_dropcap1]Waterloo, ON, January 19, 2026 [/av_dropcap1] We are happy to see Labforge and Snow Flea^TM featured in The Record today, together with our new Dual Use Tech group. Canada is facing multiple threats, including climate change, that are taking shape in the Arctic. Companies in Waterloo are rising to this challenge.

Labforge has been working with DND at various levels for almost 10 years. Snow Flea fuses imaging, radar, depth, acoustics, seismic, and RF sensing into unified situational awareness for civilian and defence use. Our connected micro-pods and towers can be used for protecting vital infrastructure, ports, and early warning sites – together with environmental monitoring, tracking endangered species, and search & rescue. Read the full article.
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January 19, 2026
Stereo Vision Explained: Bottlenose Cameras and the Importance of Confidence Maps
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Stereo Vision Explained: Bottlenose Cameras and the Importance of Confidence Maps

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Welcome back to the Labforge foundations of Machine Vision series! In this series, we are breaking down basic concepts of machine vision into simple, bite-sized pieces that are easy to understand. Whether you are a beginner or just curious about how computers interpret and interact with the visual world, this series is for you.

In this post, we will focus our attention on stereo vision. We will discuss how a stereo camera such as Bottlenose perceives depth, enabling machines to see the world in 3D. Moreover, we’ll shine a spotlight on the often-overlooked yet invaluable confidence map and its indispensable role in the post-processing of depth data. Join us as we uncover the potential of stereo vision with the Bottlenose camera.
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Depth information is lost during the formation of an image, making it difficult for a single camera to estimate the distance to objects in the real world. A stereo camera, such as the Bottlenose camera, simulates the way human eyes perceive depth by simultaneously capturing two images with slightly different viewpoints. These images are further processed to determine the depth or distance from the camera of objects in the scene. The depth of each pixel is determined by triangulating corresponding points between the left and the right images. The following figure depicts a stereo camera observing a single point P. The view cone of each camera is also shown.

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To facilitate the process of depth estimation, a stereo camera removes lens distortion and rectifies the input images using data obtained from calibration. The rectification process transforms a pair of images such that 3D points appear row-aligned on the resulting images. Stereo rectification simplifies the disparity computation problem by allowing the search for pixel correspondences on a single row. This figure from NI highlights the steps a stereo camera follows to acquire, undistort, and rectify images.

Bottlenose cameras perform undistortion and rectification as a first step to depth estimation. The camera computes the disparity of each pixel from the left image by searching the corresponding row inside the right image. The disparity map is generated as the displacement between the position of each pixel and its matching pixel position in the right image. Bottlenose uses the SGM (Semi-Global Matching) algorithm to estimate disparity. The following figure shows how the disparity of a given point P is obtained from the left image by searching the corresponding pixel inside the right image. The final disparity d is computed as d = xL-xR, where xL and xR represent the pixel position of the point in the left and right images respectively.

The depth or distance from the camera to a given point P is obtained from its disparity using the equation below, where f is the focal length of the camera, B is the baseline and d is the disparity of the point.

The accuracy of this depth estimation process is directly linked to that of the underlying disparity estimation. However, due to various factors such as lighting conditions, surface texture, and occlusions, estimating the disparity of a pixel may become challenging. This makes the resulting depth inaccurate or uncertain. To mitigate this issue, a confidence score can be produced to mark how reliable each disparity is. The generated confidence map may be used to assess the reliability or certainty of the depth measurements obtained from a stereo camera.

While both disparity and depth maps provide information about the spatial layout of a scene captured by a stereo camera, they differ in the nature of the information they convey. Disparity maps show relative disparities between corresponding points in stereo images, whereas depth maps provide absolute distance measurements in metric units. A disparity map can be further processed to represent points in 3D space. The next figure shows an example of a disparity map from a Bottlenose camera with the corresponding reprojected 3D view.

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Bottlenose stereo cameras now produce a confidence map alongside the disparity map. The confidence map can be activated either programmatically or using any GigE Vision-compliant software package. This functionality requires that the camera is properly calibrated. For detailed information on how to calibrate your Bottlenose camera, refer to our documentation page.

Labforge’s StereoViewer is a basic utility that can be used with any Bottlenose camera. It allows you to control camera settings, tune image quality, or stream images from your Bottlenose camera. Use the following steps to request and visualize confidence map and disparity from your camera using StereoViewer :
1. Click on the Connect button to select your camera
2. Open Device Control and Navigate to ImageFormatControl
3. Set ComponentSelector to Disparity
4. Set ComponentEnable to True to activate disparity computation. This assumes that your camera is properly calibrated.
5. Set ComponentSelector to Confidence
6. Set ComponentEnable to True to activate to request a confidence
The following is an example disparity map with the associated confidence map generated by a Bottlenose stereo camera. Darker areas of the confidence map highlight highly reliable disparity.

A confidence map evaluates the confidence or certainty associated with each depth or disparity measurement. A high confidence value suggests that the depth measurement is likely accurate, while a low confidence value indicates the measurement may be less reliable.

The use of a confidence map in stereo vision systems has several applications:
1. Quality Assessment: It helps in evaluating the overall quality of the depth map generated by the stereo camera. Pixel areas with high confidence values are considered more reliable, while areas with low confidence values may require further analysis.
2. Scene Understanding: By analyzing the confidence map, the system can identify areas where depth estimation is particularly challenging, such as regions with low texture or strong reflections. This information can be used to improve scene understanding and object recognition algorithms.
3. Adaptive Processing: The confidence map can be used to adaptively adjust the parameters or algorithms used for depth estimation. For example, more sophisticated algorithms or additional processing can be applied to regions with low confidence values to improve accuracy.
4. Decision Making: In applications such as industrial automation or robotics, decisions are often based on the depth data obtained from stereo cameras. By considering the confidence and depth maps, the system can make more informed decisions, especially in critical situations where accuracy is crucial.
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In this post, we learned by which process stereo cameras like Bottlenose perceive depth and enable machines to see the world in 3D. More importantly, we highlighted the use of a confidence map when dealing with unreliable depth information from challenging scenarios such as uniform walls and occlusion.
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– by G. M. Tchamgoue
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May 28, 2024
Exploring Machine Vision: Calibrating Bottlenose with MVTec HALCON
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Exploring Machine Vision: Calibrating Bottlenose with MVTec HALCON

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Welcome to the Labforge foundations of Machine Vision series! In this series, we are breaking down basic concepts of machine vision into simple, bite-sized pieces that are easy to understand. Whether you are a beginner or just curious about how computers interpret and interact with the visual world, this series is for you.

Machine vision is a field of study that enables computers to see, identify, and process images in the same way that human vision does. But how exactly does this work? What techniques and technologies are involved? And why is it so important?

Throughout this series, we will explore key topics in machine vision, starting with fundamental concepts and gradually moving toward more advanced techniques. From understanding how cameras capture images to learning about algorithms that recognize objects, each post will provide a clear and concise explanation of these fascinating topics, with accompanying sources that can be put into action with our Bottlenose Cameras and standard machine vision frameworks. In this first post, we will dive into the world of camera calibration. This crucial step ensures that the images we capture can be accurately interpreted and measured, laying the foundation for many machine vision applications.
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Pinhole Camera and Lens Distortion

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Camera calibration is a process that helps us understand how a camera sees the world. It ensures that we can accurately map points in the real world to points in an image captured by the camera. Calibration allows us to obtain precise world coordinates from images. This is crucial for various applications, including 3D modelling, 2D metrology, robotics, and augmented reality. Bottlenose can rectify lens distortions with its built-in image processor. Standard machine vision frameworks such as MVTec HALCON can be utilized to estimate the camera parameters and perform metric analysis in 2D and 3D space. The equations and the projective model are using the HALCON convention from [1].

A simple projective model is that of a pin-hole camera. In which distant objects appear smaller than closer ones. The model transforms world coordinates:
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To pixel coordinates of a specific row and column in the image:
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To understand how a camera captures a 3D point, we need to know how it projects this point onto a 2D image plane as shown below.
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A 3D point in the camera coordinate system, which is centred at the optical center of the image plane is projected onto the image plane as follows.
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Note both coordinate vectors and the focal length f are metric. The x, y, z point is the 3D location in space relative to the camera. The coordinates u and v indicate the metric coordinates of the pixel of the image sensor. To further convert u and v into pixel row and column coordinates (r, c), typically seen in images, one has to consider the sensor geometry. In this equation the image sensor is characterized by the pixel size S and the image center coordinates C.
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Typically, the world’s coordinate system does not align with the camera’s optical center. To project a point from the world coordinate system into the optical center, we need to use a homography, a transformation that aligns these coordinate systems, as follows.
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A practical example could be locating objects on a conveyor belt. It makes practical sense to set up the world coordinate system with reference to the conveyor belt rather than use the camera coordinate system.

Lenses are often not ideal and introduce distortions to the image coordinates, which need to be corrected for accurate measurements. A common distortion model used by many lenses is the polynomial distortion model which models distortions as radial and decentering. Without going into too much detail the radial distortion can be modeled by 3 K coefficients and the decentering distortion by 2 P coefficients. The model cannot be analytically inverted so all projective points cannot be computed from the distorted image plane and instead have to be computed from a corrected “undistorted” image plane. That in summary leaves the following coordinate transformations to convert from 3D world coordinates into pixel coordinates.
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1. 3D world coordinates to 3D camera coordinates
2. 3D camera coordinates to 2D image plane coordinates
3. Correcting image plane coordinates for lens distortion
4. Image plane coordinates to pixel coordinates
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Camera calibration is vital for translating the 3D world into accurate 2D images. It corrects distortions, aligns world and camera coordinates, and ensures that measurements taken from images are precise and reliable.

Stay tuned for our next blog that shows hands-on calibration of the Bottlenose camera in HALCON.

References:
1. HALCON Solution Guide to 3D Vision
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– by Thomas Reidemeister
Contact me!
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May 22, 2024
Machine Vision: Bin Picking Robots to Increase Productivity and Accuracy in Manufacturing

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Machine Vision: Bin Picking Robots to Increase Productivity and Accuracy in Manufacturing

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Bin picking, also known as part picking or piece picking, refers to the task of identifying and removing specific items (i.e., screws, pipes, lids, etc.) from a bin, box, or other container. In bin picking, machine vision refers to the use of computer vision techniques to enable a robot to locate and pick up specific objects from a jumbled assortment of objects, or a “bin,” using visual information. Machine vision systems for bin picking typically consist of a camera or other optical sensor, a computer for processing the images captured by the sensor, and algorithms that analyze the images to identify the objects of interest and determine their locations. The robot arm then uses this information to plan a path to pick up the object and move it to the desired location. Machine vision is a key enabling technology for bin picking because it allows the robot to “see” the objects and understand their locations and orientations.

There are several ways that bin picking robots can use machine vision to identify parts. One common approach is to use image processing techniques to analyze the shape, size, and other visual features of the objects in the images captured by the camera. For example, the robot might use feature detection algorithms to find the feature points of the objects, or it might use an object identification algorithm like YOLO to identify specific objects.

Industrial cameras like Bottlenose™ (which uses GigE Vision 2.1) are able to perform front-end tasks of object identification and 3D point cloud generation.

Utilizing bin picking robots in combination with machine vision, improves productivity and reduces errors in the manufacturing process. Industrial cameras like Bottlenose™ are particularly useful for bin picking tasks because they are able to process large amounts of visual data quickly and accurately, even in complex and cluttered environments. They are also able to work in a variety of lighting conditions, making them suitable for use in a wide range of factory settings. Bottlenose utilizes a powerful ISP, on-camera AI, feature point detection, and point cloud generation which can all be used to achieve a powerful bin picking system.
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We’d love to hear about your machine vision applications!

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If you are an integrator working in this space please reach out and we can work with you to achieve your goals.

Bottlenose cameras are currently in stock on Mouser.
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Related Posts

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January 5, 2023
Revolutionize Industrial Automation with Bottlenose: Now In Stock and Ready to Go

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Revolutionize Industrial Automation with Bottlenose: Now In Stock and Ready to Go

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[av_dropcap1]Waterloo, ON, December 16, 2022 [/av_dropcap1] – Labforge Inc. announced today the general availability of Bottlenose™ cameras following successful partnerships with industry and government organizations. These cameras, specially designed for use in automation, robotics, and security, feature GigE Vision 2.1, on-camera AI processing, feature point detection and matching, up to 4K disparity resolution, and a powerful ISP to navigate complex lighting situations.

Labforge has supplied Mouser Electronics with their first shipment of Bottlenose cameras, which are now in stock and ready for purchase. The new Bottlenose camera is expected to catalyze adoption of smart technologies in industrial automation, robotics, and security industries, thanks to its easy-to-use UI and accessible pricing. Both stereo and monocular versions are now available at a suggested retail price of $1,000 USD. The flexibility allowed by the CS lens mount, coupled with advanced on-camera processing and 4K resolution, will encourage near-endless creativity in solution building across industries.
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“We’re excited to see how global integrators, roboticists, and engineers will use Bottlenose to see the world in new ways,” said Kendra Serbinski, Business Development Manager at Labforge.

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Bottlenose cameras can be purchased through Labforge’s distributor, Mouser Electronics, or for custom orders, please contact Labforge directly. Labforge Bottlenose cameras will be on display at CES in January 2023, in the TAEC suite (invitation only) and the TDK booth (16181 in Central Hall of the Las Vegas Convention Center).
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About Labforge

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Labforge is a Waterloo, Ontario based technology company that designs, develops, and sells smart cameras. Their cameras are used for automation, safety, and security. Labforge is funded in part by the Ontario Centers of Innovation, Department of National Defence, and Innovative Solutions Canada. Customers and partners include University of Waterloo, Royal Canadian Air Force, Toshiba, and Transport Canada.
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December 16, 2022
Latest Updates for Bottlenose

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LATEST UPDATES FOR BOTTLENOSE

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December 14, 2022

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As we head into the holiday season, we’re excited to announce that Bottlenose (both stereo and monocular versions) is now in stock on Mouser.com!
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A Bottlenose monocular camera.

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We’d like to thank everybody for their support on this whirlwind adventure!
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October 17, 2022

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Great news! We have fixed the design issues related to the Ethernet PHY along with some other improvements. These were sent to the factory a few weeks ago. Fingers crossed. Test samples will be ready next week. The dev team will be at the factory testing.
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We have also finalized our simple but functional packaging as we prepare for shipping!

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September 2, 2022

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This week we took Bottlenose to a Nemko lab to test inside an RF isolated chamber and ensure it’s not producing any harmful or disturbing radio waves.

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Bottlenose being set up in the anechoic chamber.

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Thomas and Martin configuring the camera to run some very intense stress testing firmware. This firmware runs all of the computer vision and AI accelerators at the same time. Since Bottlenose’s ASIC processor has a heterogenous architecture, its best to test all the clock frequencies, and make sure none cause harmful interference.

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Bottlenose plugged into the test setup on a rotating turntable.

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We also tested for conducted emissions that may show up on the ethernet or power cable.

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We are excited to share that the testing was a success!

All of this is happening in parallel to our efforts in unblocking production. We are still solving our Ethernet PHY + ASIC design and supply related issue from earlier and hope to have production going again soon.

Hope you all have a great Labor Day long weekend!

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AUGUST 25, 2022

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Look how tiny AMPA is! This is the brain within Bottlenose!

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AUGUST 24, 2022

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Bottlenose development version undergoing bring-up and first firmware flash via an easy to use JTAG cable. We have designed the camera to be easy to repair. This also allows community members to openly modify the camera for their needs.

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Undergoing open-frame thermal testing. Camera is running a stress test loop with as many image processing and computer vision tasks running in parallel as possible. Low power dissipation allows use cases in many industrial and robotics applications.

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AUGUST 17, 2022

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Our engineering team has been curating a playlist while building Bottlenose – click the image below to listen!

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AUGUST 11, 2022

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Very excited today to see the first ever pictures taken from the latest design iteration of the Bottlenose cameras! Great quality! They are compressed for this blog, but the camera outputs these in raw format.

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Taken with a zoom lens (Theia SL1250). To view the original 4K image click here.

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Taken with a wide-angle lens (Theia SL410). To view the original 4K image click here.

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Understandably, Martin was super excited for this moment!

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AUGUST 8, 2022

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Welcome to the first post for following along with the build progress for Bottlenose. The original plan was to ship the cameras this month. The team is working very hard and we are very thankful to our partners and vendors who have done a tremendous job!

We would like to use this blog post to provide updates and some sneak peaks behind-the-scene.

The first batch of PCB’s arrived on Friday, the 5^th of August 2022. These included our, soon to be released, AMPA™ SOM and the carrier boards that together form Bottlenose cameras. Boards look great and MCAD-ECAD integration works well.
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Bottlenose monocular in 3D print and stereo in CNC machined aluminum with PCBs inside.

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Reverse side of the stereo camera where you can see that care has been taken to make this camera easy to open and repair. JTAG access is provided from the back for advanced users.

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Monocular board from the front, showing the IMX577 image sensor and AMPA underneath.

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CS-mount lens housing (minus the threads), with IMX577 visible inside.

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Bottlenose stereo version, fully assembled.

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Stereo board with AMPA undergoing first boot-up. Power consumption looks very low as per design! In our stress tests we did not exceed 6.5W – this test included AI, depth, and other processing.

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We realize soon that some of the connections in our schematics were reversed and that some other design errors will cause issues, and prepare a pizza for sustenance.

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We had to add a patch fix to AMPA, but it’s super tiny and requires delicate work.

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One of the patches will reverse two of the tiny pins you see here on this ethernet PHY. The clock and control lines got reversed.

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Photo from microscope of the patch.

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Its alive!! Once patched, Bottlenose shows up on our GigE Vision monitoring software.

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Patch shows fully working ethernet.

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Not so good news: AMPA and baseboard need some minor revisions. We could patch these as above and ship or we could do a design change and ship in a few weeks. AMPA is a very high-density board and requires special processes. Stay tuned for further updates!

Click here to read more about Bottlenose smart cameras.

What do you think? We would love to hear suggestions or feedback. Please let us know if you have feature requests by clicking the “Get in touch” button below.

Bottlenose is currently available for purchase on Mouser Electronics.
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December 14, 2022
Critical Mineral Identification Using Cameras

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CRITICAL MINERAL IDENTIFICATION USING CAMERAS

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The Canadian government has put forth a goal to reduce our net carbon emissions to zero by 2050. One avenue the Federal government is exploring to help achieve this goal is the creation of the Critical Minerals Research, Development and Demonstration (CMRDD) program. This program has a budget of $10.95 million to contribute to funding for pilot plants and demonstrated projects with a focus on developing critical minerals value chains.¹

In Ontario, the provincial government will also be investing in the Critical Mineral Innovation with a budget of $2 million in 2022-23 and a further $3 million in 2023-24 to create a Critical Minerals Innovation Fund. These funds are aimed at supporting innovative projects that develop ways to extract and process critical minerals with the longer-term goal of having Ontario become a leader in battery technology, electric and hybrid vehicles, and advanced manufacturing.²
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Cameras and Critical Mineral Research

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One promising avenue in critical mineral research involves the use of cameras and deep learning as a means of classifying each mineral constituent in an ore. Being able to quickly and accurately identify mineral components in the ore allows for streamlined and more efficient mineral processing. Integrating artificial intelligence and computer vision will allow automated systems to more quickly identify and understand the ore moving throughout a mineral processing plant.³ Knowledge and identification of the particle size, shape, useful mineral content, particle size composition, and mineralogical composition are essential to mineral processing. The use of cameras and deep learning allows for adjustment and optimization of crushing and grinding operations at the plant.⁴, ⁵
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fig. 1 — (left) Original photomosaic of a sample surface approximately 35 mm in diameter

(right) RGB sub-image of 600×600 pixels as used for image segmentation.⁶
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RGB (Red, Green, Blue) cameras have been used in mineral mining applications to grade samples based on color information.⁷ In addition to color information, RGB images also contain necessary textural information as well.⁸
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fig. 2 — (top) RGB histograms for two known minerals

(bottom) Multiple Thresholding segmentation.⁸
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The Future of Cameras and Critical Mineral Identification

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Sensor based sorting is being touted as the way forward in the mining industry to mitigate working with declining ore grades and complex ore types. Optical color sorting has been used to successfully separate chromite and gold ore. Near-infrared (NIR) sensing is also used to determine the mineralogical composition of an ore sample and are currently utilized for sorting industrial materials including talc and borates.⁹
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Bottlenose™ and Applications in Mining

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Bottlenose™ sensors are a great addition to the critical mineral mining and processing industry for several reasons. 20.5TOPs of processing power allows for advanced on-camera object detection, segmentation, annotation, and classification – features which are crucial for advancing Canada’s critical mineral research.¹⁰ Bottlenose™ sensors are also equipped with a powerful ISP which makes managing complex lighting situations a breeze. The ISP processes dual 4K images simultaneously and includes lens shading correction, color correction matrix, auto white balance, undistort, amongst a host of other image processing options.

If you are looking to get involved in this exciting field of mineral identification, please contact us or try our camera to gain a competitive edge.
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July 4, 2022
Labforge and Mouser Electronics Announce Global Distribution Agreement

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Labforge and Mouser Electronics

Announce Global Distribution Agreement

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[av_dropcap1]Waterloo, ON, June 28, 2022 [/av_dropcap1] Labforge, Inc. announces a global distribution agreement with Mouser Electronics, Inc., a leading semiconductor and electronic component distributor. According to the agreement, Mouser will offer their customers Labforge’s Bottlenose™ family of high-resolution smart cameras for robotics, industrial automation, Internet of Things (IoT), and security applications.
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“The groundbreaking Bottlenose family of cameras with on-board AI, HDR, depth computation, and feature point matching will catalyze the rapidly advancing industrial automation, robotics, and defense industries,” said Andy Kerr, Vice President of Supplier Management at Mouser Electronics. “We’re ecstatic to offer Labforge’s innovative cameras in our continuing quest to advance these sectors.”

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“Working with Mouser Electronics will allow global access for integrators, engineers, and roboticists to acquire and integrate our high-performance smart cameras into their new projects,” said Kendra Serbinski, Business Development Manager at Labforge. “Mouser’s leading distribution expertise and best-in-class service enables innovation across industries and geographies, and we are pleased to be a part of their efforts.”

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Bottlenose cameras offer built-in capability that does not require any additional software licenses or subscription fees. All processing happens on the camera itself, and customers don’t have to pay any third-party data charges or cloud fees either. Stereo versions boast dual image sensors, while both monocular and stereo versions have hardware-synchronized, triple-axis gyroscopes, accelerometers, and magnetometers.
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Each camera is equipped with a powerful 20.5TOPS 16nm chip with ten processors, four DSPs, and eight types of accelerators. This makes it possible for users to perform on-camera object detection and classification, compute depth, segmentation, and feature tracking. Engineers and integrators can use a simple microcontroller to read results via the serial port, as there are no minimum requirements on the client side. Advanced applications are also accommodated via popular frameworks like MVTec HALCON and ROS via the GigE Vision 2.1 API.
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Bottlenose cameras offer an ideal solution for a variety of applications including service and delivery robots, manufacturing automation, AgTech robots, VRU protection, and food and beverage processing.
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About Mouser Electronics

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Mouser Electronics, a Berkshire Hathaway company, is an authorized semiconductor and electronic component distributor focused on New Product Introductions from its leading manufacturer partners. Serving the global electronic design engineer and buyer community, the global distributor’s website, mouser.com, is available in multiple languages and currencies and features more than 6.8 million products from over 1,200 manufacturer brands. Mouser offers 27 support locations worldwide to provide best-in-class customer service in local language, currency and time zone. The distributor ships to over 650,000 customers in 223 countries/territories from its 1 million-square-foot, state-of-the-art distribution facilities in the Dallas, Texas, metro area.

For more information, visit https://www.mouser.com/.
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About Labforge

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Labforge is a Waterloo, Ontario based technology company that designs, develops, and sells smart cameras. Their cameras are used for automation, safety, and security. Labforge is funded in part by the Ontario Centers of Innovation, Department of National Defence, and Innovative Solutions Canada. Customers and partners include University of Waterloo, Royal Canadian Air Force, Toshiba, and Transport Canada.
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June 28, 2022