Whether Augmented, Mixed and Virtual Reality?

23/03/2020

XR is a term which has become more prominent in the last few years. It encapsulates virtual, augmented, and mixed reality topics. The definition of each of these has become saturated in the past decade, with companies using their own definitions for each to describe their products. The new IDTechEx Report, “Augmented, Mixed and Virtual Reality 2020-2030”, distils this range of terms and products, compares the technologies used in them, and produces a market forecast for the next decade.

The report discusses 83 different companies and 175 products in VR (virtual reality), AR (augmented reality) and MR (mixed reality) markets. This article specifically discusses the findings on the virtual reality market.

Virtual reality (VR) involves creating a simulated environment which a user can perceive as real. This is achieved by stimulating the various senses with appropriate signals. This is most commonly visual (via displays and optics) and auditory (via headphones or speakers) signals, but also increasingly involves efforts around haptic (touch) sensations. The generation of realistic virtual environments requires the generation of appropriate stimuli and systems to direct how the stimuli should change, whether automatically or due to user interaction. As such, this relies on a variety of components and systems including displays, optics, sensors, communication and processing, delivered via both hardware and associated software to generate this environment. 

There are three main groups of VR headset – PC VR, Standalone VR and Smartphone VR.  PC VR has a user interface & display worn on the body, but the computing and power are offloaded to the external computer. This is where most of the commercial hardware revenue is made today. Standalone VR is a dedicated standalone device (no tethering) with all required computing and components on board. Finally, smartphone/mobile VR uses the smartphone processor, display and sensors used to power VR experience, with only a very cheap accessory necessary to convert to VR. The report discusses the revenue split for these three sectors in full, and an example image is shown in the figure on right.

The report discusses the likelihood of a shift in the devices used by consumers, for example from a PC VR to a standalone VR headset. This is because it would provide greater freedom of movement and accessibility for different use cases. One example of a standalone VR product is the Oculus Quest device, released in 2019. This was one of the first devices to be standalone for a gaming purpose, and it has all the heat management and processing systems on the headset itself. Oculus is one of the big players in the VR market, and have a range of products, some of which are shown in the table and images below.

These headsets provide a range of experiences for the user, at different price points. After being founded in 2012, Oculus was bought by Facebook for $2.3bn in 2014, it has continued to grow and produce VR products for a range of markets. Details of the growth of the VR market are included in the report for a range of companies, and their different use cases. The overall market is expected to grow, as shown in this plot below.

The full image is available in the report

VR, AR & MR, as with nearly any technology area, must build on what has come before. The existing wave of interest, investment and progress in the space has been built on top of the technology which has been developed in other areas, for example from the smartphone. Many components in VR, AR & MR headsets, from the displays used, to the sensor integration (from IMUs, to 3D imaging and cameras, and more) to the batteries and power management, and so on, all directly built on the components which were invested so heavily in around the smartphone. This technology is heavily invested, targeting the future potential of XR headsets. This report provides a complete overview of the companies, technologies and products in augmented, virtual and mixed reality, allowing the reader to gain a deeper understanding of this exciting technology.

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Augmented and mixed reality: what is it, and where is it going?

10/03/2020

XR is a term that has become more prominent in the last few years. It encapsulates virtual, augmented, and mixed reality topics. The definition of each of these has become saturated in the past decade, with companies using their own definitions for each to describe their products. The new IDTechEx Report, “Augmented, Mixed and Virtual Reality 2020-2030”, distils this range of terms and products, compares the technologies used in them, and produces a forecast for the market next decade. This premium article discusses AR (augmented reality) and MR (mixed reality) in more detail.

The report discusses 83 different companies and 175 products in VR (virtual reality), AR (augmented reality) and MR (mixed reality) markets. This promotional article specifically discusses the findings from this report of the augmented and mixed reality markets.

Augmented Reality (AR) and Mixed Reality (MR) are two technologies which have become more prominent in the past ten years. AR is the use of computer technology to superimpose digital objects and data on top of a real-world environment. MR is similar to AR, but the digital objects interact spatially with the real-world objects, rather than being superimposed as “floating images” on top of the real-world objects. AR and MR are also closely related to VR. There is a cross-over in application and technology, as some VR headsets simulate the real space and then add in extra artificial content for the user in VR. But for this article, AR and MR products are considered those which allow the user in some way to directly see the real-world around them. The main target sectors of AR and MR appear to be in industry and enterprise markets. With high costs of individual products, there appears to be less penetration into a consumer space.

AR and MR products are being used in a variety of settings. One way they are being used is to solve a problem called “the skills gap” This describes the large portion of the skilled workforce who are expected to retire in the next ten years, leading to a loss of the knowledge and skills from this workforce. This knowledge needs to be passed on to new, unskilled, employees. Some companies propose that AR/VR technology can fill this skills gap and pass on this knowledge. This was one of the key areas discussed at some events IDTechEx analysts attended in 2019, in researching for this report.

AR use in manufacturing and remote assistance has also grown in the past 10 years, leading to some AR companies targeting primarily enterprise spaces over a consumer space. Although there have been fewer direct need or problem cases which AR can solve for a consumer market, smartphone AR can provide an excellent starting point for technology-driven generations to create, develop, and use an XR enabled smartphone for entertainment, marketing and advertising purposes. One example of smartphone AR mentioned in the report is IKEA place. This is an application where a user can put a piece of IKEA furniture in their room to compare against their current furniture. It allows users a window into how AR can be used to supplement their environment and can be used in day to day activities such as purchasing and visualising products bought from an internet marketplace.

AR and MR companies historically have typically received higher funding per round than VR – e.g. Magic Leap which has had $2.6Bn in funding since its launch in 2017, but only released a creator’s edition of its headset in 2019. AR and MR products tend to be more expensive than VR products, as they are marketed to niche use cases. These are discussed in greater detail in the report, for example the below plot which shows this tendency for AR/MR products to be more expensive than VR products.
The report compares both augmented and mixed reality products and splits them into three categories: PC AR/MR, Standalone AR/MR and Smartphone/mobile AR/MR. PC products which need a physical PC attachment, standalone products which do not require a PC, and smartphone products – those which use a smartphone’s capabilities to implement the immersive experience. Standalone AR/MR have had more distinct product types in the past decade, and this influences the decisions made when forecasting the future decade to come.

The report predicts an AR/MR market worth over $20Bn in 2030, displaying the high interest around this technology. This report also provides a complete overview of the companies, technologies and products in augmented, virtual and mixed reality, allowing the reader to gain a deeper understanding of this exciting technology.

In conclusion, VR, AR & MR, as with nearly any technology area, must build on what has come before. This technology is heavily invested, targeting the future potential of XR headsets. “Augmented, Mixed and Virtual Reality 2020-2030” provides a complete overview of the companies, technologies and products in augmented, virtual and mixed reality, allowing the reader to gain a deeper understanding of this exciting technology.


No escape even for agrochemicals!

28/09/2017
In this article key points that are covered in depth in the IDTtechEX published report “Agricultural Robots and Drones 2017-2027: Technologies, Markets, Players” by Dr Khasha Ghaffarzadeh and Dr Harry Zervos are discussed. 

New robotics is already quietly transforming many aspects of agriculture, and the agrochemicals business is no exception. Here, intelligent and autonomous robots can enable ultraprecision agriculture, potentially changing the nature of the agrochemicals business. In this process, bulk commodity chemical suppliers will be transformed into speciality chemical companies, whilst many will have to reinvent themselves, learning to view data and artificial intelligence (AI) as a strategic part of their overall crop protection offerings.

Computer vision
Computer vision is already commercially used in agriculture. In one use case, simple row-following algorithms are employed, enabling a tractor-pulled implement to automatically adjust its position. This relieves the pressure on the driver to maintain an ultra-accurate driving path when weeding to avoid inadvertent damage to the crops.

The computer vision technology is however already evolving past this primitive stage. Now, implements are being equipped with full computer systems, enabling them to image small areas, to detect the presence of plants, and to distinguish between crop and weed. The system can then instruct the implement to take a site-specific precision action to, for example, eliminate the weed. In the future, the system has the potential to recognize different crop and weed types, enabling it to take further targeted precision action.

This technology is already commercial, although at a small scale and only for specific crops. The implements are still very much custom built, assembled and ruggedized for agriculture by the start-ups themselves. This situation will continue until the market is proven, forcing the developers to be both hardware and software specialists. Furthermore, the implements are not yet fully reliable and easy to operate, and the upfront machine costs are high, leading the developers to favour a robotic-as-a-service business model.

Nonetheless, the direction of travel is clear: data will increasingly take on a more prominent (strategic) role in agriculture. This is because the latest image processing techniques, based on deep learning, feed on large datasets to train themselves. Indeed, a time-consuming challenge in applying deep learning techniques to agriculture is in assembling large-scale sets of tagged data as training fodder for the algorithms. The industry needs its equivalents of image databases used for facial recognition and developed with the help of internet images and crowd-sourced manual labelling.

In not too distant a future, a series of image processing algorithms will emerge, each focused on some set of crop or weed type. In time, these capabilities will inevitably expand, allowing the algorithms to become applicable to a wider set of circumstances. In parallel, and in tandem with more accumulated data (not just images but other indicators such NDVA too), algorithms will offer more insight into the status of different plants, laying the foundation of ultra-precision farming on an individual plant basis.

Agriculture is a challenging environment for image processing. Seasons, light, and soil conditions change, whilst the plant themselves transform shape as they progress through their different stages of growth. Nonetheless, the accuracy threshold that the algorithms in agriculture must meet are lower than those found in many other applications such as autonomous general driving. This is because an erroneous recognition will, at worse, result in elimination of a few healthy crops, and not in fatalities. This, of course, matters economically but is a not safety critical issue and is thus not a showstopper.

This lower threshold is important because achieving higher levels of accuracy becomes increasingly challenging. This is because after an initial substantial gain in accuracy improvement the algorithms enter the diminishing returns phase where lots more data will be needed for small accuracy gains. Consequently, algorithms can be commercially rolled out in agriculture far sooner, and based on orders of magnitude lower data sizes and with less accuracy, than in many other applications.

Navigational autonomy
Agriculture is already a leading adapter of autonomous mobility technology. Here, the autosteer and autoguide technology, based on outdoor RTK GPS localization, are already well-established. The technology is however already moving towards full level-5 autonomy. The initial versions are likely to retain the cab, enabling the farmer/driver to stay in charge, ready to intervene, during critical tasks such as harvesting. Unmanned cable versions will also emerge when technology reliability is proven and when users begin to define staying in charge as remote fleet supervision.

The evolution towards full unmanned autonomy has major implications. As we have discussed in previous articles, it may give rise to fleets of small, slow, lightweight agricultural robots (agrobots). These fleets today have limited autonomous navigational capability and suffer from limited productivity, both in individual and fleet forms. This will however ultimately change as designs/components become standardized and as the cost of autonomous mobility hardware inevitably goes down a steep learning curve.

Agrobots of the future
Now the silhouette of the agrobots of the future may be seen: small intelligent autonomous mobile robots taking precise action on an individual plant basis. These robots can be connected to the cloud to share learning and data, and to receive updates en mass. These robots can be modular, enabling the introduction of different sensor/actuator units as required. These robots will never be individually as productive as today’s powerful farm vehicles, but can be in fleet form if hardware costs are lowered and the fleet size-to-supervisor ratio is increased.

What this may mean for the agrochemicals business is also emerging. First, data and AI will become an indispensable part of the general field of crop protection, of which agrochemical supply will become only a subset, albeit still a major one. This will mandate a major rethinking of the chemical companies’ business model and skillsets. Second, non-selective blockbuster agrochemicals (together with engineered herbicide resistant seeds) may lose their total dominance. This is because the robots will apply a custom action for each plant, potentially requiring many specialized selective chemicals.

These will not happen overnight. The current approach is highly productive, particularly over large areas, and off-patent generic chemicals will further drive costs down. The robots are low-lying today, constricting them to short crops. Achieving precision spraying using high boys will be a mechanical and control engineering challenge. But these changes will come, diffusing into general use step by step and plant by plant. True, this is a long term game, but playing it cannot be kicked into the long grass for long.

@IDTechEx #Robotics #Agriculture #PAuto