SLAM Technology Market: A Quick Glimpse into its Diverse Functional Areas
Posted: Sep 05, 2022
Until recently, robots worked in zones with predefined dimensions of the environment. Whether it was a hospital or a warehouse, robots had pre-existing knowledge of the environment and performed a fixed set of tasks. However, with increasing autonomy, it becomes difficult for robots to navigate dynamic, complex, and new terrains without prior knowledge.
This is where SLAM technology can make a drastic difference for such devices since it collects spatial information, constructs a virtual map, and updates real-time coordinates to help navigation. As per Inkwood Research, the global SLAM technology market is set to project a CAGR of 40.46% during the forecast period, 2022-2030.
Developed in the 1980s, simultaneous localization and mapping (SLAM) is a technology process that facilitates autonomous navigation for robots through new surroundings without maps. The technology uses data from the robots’ onboard sensors, processing it using computer vision algorithms to recognize the environment’s features. This further aids the SLAM in building a rough map and making an initial estimate of the robots’ position.
Accordingly, SLAM has the potential to be transformative for different domains, from robotics and augmented reality to autonomous vehicles and unmanned aerial vehicles.Here’s a glimpse into its diverse functional areas:
- Augmented Reality
In augmented reality (AR), two aspects of information are required to offer precise augmented visuals of the real world. These include the current real-world view that needs to be augmented and the virtual object geometry and its exact registration with the real world.
Accordingly, AR applications do not have the required information regarding virtual objects and overlays to display in an unknown environment. As a result, the possible integrations of SLAM systems have been considered. Additionally, SLAM can facilitate the creation of models for end-users that require annotations and tracking on the fly.
For instance, Apple introduced ARKit in June 2017. It uses floor recognition and position tracking to place AR objects. However, Google Tango is the more advanced SLAM technology that creates a network of the surrounding, tells the floor’s location, and identifies walls and objects, allowing everything around the user to be elements of interaction.
Our evaluations state that Augmented Reality (AR) is the fastest-growing application in the global SLAM technology market, estimated to register a CAGR of 55.85% by 2030.
In terms of sensors, SLAM is divided into visual SLAM (vSLAM) and LiDAR SLAM. According to an article on Wevolver, researchers at the University of Michigan developed an ultra-energy-efficient processor to aid autonomous micro aerial vehicles in navigating their environment. Also, they leveraged highly efficient deep learning algorithms to realize visual simultaneous localization and mapping (vSLAM) technology in a single chip that measures a little over a centimeter.
Since SLAM’s goal is to construct a 3D map as the robot navigates its surrounding, it is also widely used in different sensing and location techniques like cameras, GPS, and LiDAR. Also, visual SLAM relies solely on cameras and is the preferred method for autonomous aerial vehicles.
Furthermore, autonomy in the drone industry is gradually becoming popular, given the demand for drone operations beyond the visual line of sight. Additionally, drones with commercial applications are gradually becoming autonomous to minimize human interaction and reduce the total expenditure on drone operation.
Moreover, the incorporation of SLAM technology and artificial intelligence renders drones capable of flying and gathering data. It also enables drones to simultaneously localize and map its surrounding. Several leading manufacturers are already developing SLAM-based solutions for drones. For instance, Parrot has launched PARROT S.L.A.M.dunk, an integrated kit for developers to manufacture advanced navigation applications for unmanned aerial vehicles.
As per our research, Extended Kalman Filter (EKF) is the largest revenue-generating type in the global SLAM technology market, with a revenue share of 56.70% in 2021.
The mining sector is a lucrative area with regard to advanced autonomous robots’ implementation. This is attributed to their benefits, like minimal human presence in hazardous locations, robot action repeatability, and increased safety. Hence, SLAM techniques are used in such devices to facilitate simultaneous localization and navigation in the working environment.
Additionally, SLAM technology can enable the acquisition of dense spatial data in the form of 3D point clouds in specific cases. This can be further used for different 3D modeling and spatial analysis purposes. Also, drones are being sent to hitherto unexplored areas underground using LiDAR and SLAM technologies, where GPS navigation is not feasible. These include abandoned declines, mine shafts, stopes & voids, sections compromised by illegal mining or unsupported ground conditions, etc.
Moreover, such technologies can help miners produce comprehensive images of their mission-critical, high-value assets in mines. This can be achieved through the active or passive deployment of digital scanning and mapping payloads.
According to our estimations, 3D SLAM is the fastest-growing mapping technique in the global SLAM technology market, expected to record a CAGR of 46.63% by 2030.SLAM Technology: Future Implications
SLAM technology has witnessed substantial progress over the last 30 years. SLAM and related techniques, like visual-inertial odometry, are increasingly deployed in several real-world settings, from autonomous cars to mobile devices. In addition, SLAM techniques will have more preference in the future for obtaining reliable metric positioning in situations where infrastructure-based solutions like GPS are unavailable.
Furthermore, the aforementioned varied functional areas define the growth trajectory of the global SLAM technology market. Moreover, with increasing developments and innovations in the form of new applications, sensors, and computation tools, the answer to whether or not SLAM is necessary, is affirmative.
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