We share the basic concept of SLAM with cloud/ 5G network and its example in autonomous mobile robots applications in our previous article. In this second part, we are going to share examples of visual positioning, AR cloud, and autonomous driving applications.

We believe this will help you understand various use cases where SLAM and 5G/Cloud can play a role together.

Use-case examples in visual positioning

We have seen an increasing demand for use cases in the visual positioning of people or machines in an indoor setting together with AMR.

One example is understanding the positions of operators in industrial facilities such as a warehouse or a power plant so that the user can provide a warning to operators when they get close to a hazardous area. The user can also use this information to improve the productivity of operators.

In these applications, the cost of the hardware they wear needs to be very affordable as the solution requires as many units as the number of operators. Generally, more than 100 units are required in a warehouse.

In such a scenario, offloading SLAM processing onto the cloud is the key to minimizing hardware costs. Figure 1 illustrates a suggested architecture to achieve this.

Figure 1: High-level architecture of SLAM on cloud example of operator positioning in a warehouse.

A stereo camera can be used to create a map instead of a mono camera, resulting in more stable performance. However, each positioning/ tracking device mounted can be a mono camera mounted on the helmet to minimize the hardware cost.

This application doesn’t need 5G necessarily, but 5G ensures mission-critical connectivity and low latency for these operators.

Another noteworthy example of visual positioning is forklift position tracking.

Many companies deploy a fleet of manual forklifts and want to understand how efficiently they operate and improve overall productivity. The key to achieving this is recognizing the position of the forklifts.

Since multiple forklifts would be deployed, the hardware costs need to be a bare minimum, and 2D or 3D lidars aren’t an option. We can create a map with one forklift by scanning the area and then using this map to get the positions of forklifts in the area using their stereo camera or even a mono camera, depending on the required accuracy. This architecture is further painted in figure 2.

Figure 2: High-level architecture of SLAM on cloud example of forklift position tracking

This scenario doesn’t demand the position of forklifts at every camera image frame (such as 30 fps) but does require something around 1 FPS. So the system could only send 1 pair of images (from the left and right lenses) every second to understand the overview of forklift operations.

As you may have already understood, this application doesn’t need 5G, but 5G would make communication more reliable.

AR Cloud use case example of SLAM and 5G/Cloud

Augmented Reality (AR) Cloud implies an AR application using a map stored on the cloud and also using the position of the device on that map. This is another common usage of SLAM.

However, it is hard to meet the computation and memory requirements for augmented reality applications [1].

For example, a person holds his smartphone and looks around with it. The smartphone understands where it is and which direction the person is facing.

So an AR cloud app can show the direction to a specific location based on this or overlay ads on the actual scenery on the screen. Figure 3 shows the simple architecture of this application.

Figure 3: High-level architecture of SLAM on cloud example of AR cloud.

Autonomous driving using SLAM and 5G/Cloud

SLAM on the cloud provides another level of scalability and flexibility to autonomous driving applications.

One of the main challenges of autonomous driving at scale is maintaining up-to-date maps on each of the vehicles. A straightforward solution is to keep a master map on the cloud and let each vehicle consume the necessary part of this map as they drive.

Now the problem gets simpler, as the task is to maintain that single map. So how can we keep this single map up-to-date?

Figure 4: High-level architecture of SLAM on cloud example in autonomous driving.

One approach is using sensor data from each vehicle and updating the single map whenever needed using this data. Thus each vehicle not only consumes the map data but also acts as a mapping agent, as seen in figure 4.

The system could adjust how what portion of the map each vehicle downloads based on the vehicle speed and the network speed. As you already might have guessed, 5G can help download the partial map with low latency and stable connectivity to each vehicle.

Final words

This article showed how cloud and 5G communication technology could help SLAM be widely adopted across multiple use cases.

The examples we showed prove that this crossover of technologies adds more flexibility to how the SLAM can be used. Of course, the use cases we’ve listed here may not be all, and you may have your specific use case requirements for the business problems you have at hand.

Say hi, and we’d be happy to help you transform your business through our SLAM solutions!

References

[1] Jiao, J., Yun, P. and Liu, M. (2017). A Cloud-Based Visual SLAM Framework for Low-Cost Agents. 471–484. [PDF]

The post Endless Possibilities with SLAM and 5G/Cloud Technology Together (Part 2) first appeared on Kudan global.

3D-SLAM is of the instrumental technologies across several use-cases involving robotics, mapping, drones, autonomous vehicles, and AR/VR. Not second to any, the cloud and 5G communications are disrupting the technology industry with multiple use cases.

What possibilities can be unlocked if we can combine these two disruptive technologies in use cases?

At Kudan, we have already delivered multiple projects in the crossover: SLAM on the cloud and SLAM using 5G communications. This article will further explore and paint a clear picture of some of the problems that can be solved by the 3D-SLAM and 5G/cloud technologies.

As a first step, let’s understand how these technologies can be combined in the use cases.

Understanding the crossover of SLAM and Cloud technology

Kudan Visual SLAM can run 5–10 times faster than other 3D SLAM algorithms (eg: ORB-SLAM2 on some Arm-based processors). Still, as it requires iteration of optimization using a large number of 3D point information, 3D-SLAM can be heavy and process-intensive for tight processing budget applications.

As a result, not all applications can afford the processing hardware that is suitable for 3D SLAM [1]. Often this acts as a blocker for adopting 3D SLAM for use cases. The ability to offload the SLAM process elsewhere can help adopt the technology into many more use cases.

Then cloud comes into play here. We can offload the SLAM process to the cloud. Let us explain through an example: for robotic applications, the robots can send the images from their sensors to the cloud. The SLAM process can now run on the cloud and send the information only regarding the poses back to the robot. The robot can use this information for its own control and motion.

This is truly disruptive and SLAM on the cloud already has significant potential to expand the usage of SLAM to multiple use cases. The figure 1 below does a great job of explaining the architecture in the usage of cloud alongside SLAM.

Figure 1: High-level architecture of Visual SLAM on the cloud.

Deteriorating accuracy and role of 5G communication technology

The problem we faced for some SLAM-on-cloud projects was the latency and robust connectivity that wasn’t up to the mark when sending the image stream to the cloud and receiving the required information back.

For use cases requiring continuous pose information at 10Hz or more, this is crucial. And as a result, we experienced deteriorated accuracy and an increased risk of collision in the robots. We needed a way to communicate better between the robots and the cloud.

That’s when 5G technologies becomes relevant with SLAM. It provides higher speeds, superior reliability, and negligible latency. In our context, image sequences from multiple robots can be sent through 5G technology without significant latency. It enabled the application to use low-cost, low-power hardware for edge devices while still benefiting from 3D SLAM features.

You now probably have a good understanding of the architecture and the usage of both cloud and 5G technology for SLAM. Let’s now visit a use-case example that leverage these technologies.

Use-case example: Autonomous Mobile Robots (AMRs)

Visual SLAM functionality can be added to an existing autonomous mobile robot (AMR) to make its localization performance more robust and stable.

Many AMRs use 2D-Lidar SLAM for localization. Though it shows acceptable performance for SLAM-friendly environments, it struggles in environments where the scenery is constantly changing or occasional outdoor operations such as between factories.

Visual SLAM has a clear advantage when fused with its 2D counterpart in these scenarios. Figure 2 illustrates how the Visual SLAM can be used with the cloud service.

Figure 2: High-level architecture of Visual SLAM on cloud example in robotics applications.

As shown in the architecture above, you can obtain the pose from Visual SLAM through the cloud and fuse it with 2D-Lidar SLAM pose to get more redundancy.

The frequency of sending images can be flexible depending on the purpose of Visual SLAM and the availability of 5G.

In instances where there is a good 5G network available, you may send an image stream at 30 frames per second (FPS). When not available, you may send an image every second (1 FPS) and use it as an aiding approach on top of 2D-Lidar SLAM.

Another option is to dynamically change between Visual SLAM and 2D Lidar SLAM; for instance, when the robot is outdoor, it sends images more frequently so that it can rely solely on Visual SLAM, and when indoors, it sends at a lower frequency indoors to limit network usage.

As you can see, you suddenly have many options to enhance the system’s overall performance.

Final words

Did you really think that’s all the use case examples of SLAM with 5G/Cloud? There’s plenty more. But let’s stop here for now as we already introduced many new ideas around 3D SLAM.

Many more practical examples will be discussed in more detail in part 2 of this article which includes examples from Visual positioning and autonomous driving. So you can have an even more detailed understanding of the applications of SLAM. Stay tuned!

Meanwhile, feel free to say hi, and we’d be happy to help you transform your business through our SLAM solutions!

References

[1] Kamburugamuve, S., He, H. & Fox, G. and Crandall, D. (2016). Cloud-based Parallel Implementation of SLAM for Mobile Robots. [PDF]

The post Endless Possibilities with SLAM and 5G/Cloud Technology Together (Part 1) first appeared on Kudan global.