Goals of the CanSLAM Circuit
The overarching goal of the CanSLAM Circuit is to increase awareness of, manage expectations for, and assist in the community development of mobile mapping systems, their applications, and the computer vision processes that support them.
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Mobile mapping systems and the simultaneous localization and mapping (SLAM) algorithms that support them promise to usher in not only an era of rapid and accurate spatial data collection, but a second promise of rapid and accurate spatial data extraction though computer vision and analytics. These systems combined have the potential to change the face of operational awareness and planning.
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To this end, our operational goals are as follows:
1) Introduce
Introduce industry to mobile mapping and SLAM manufacturers
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This starts with a list of companies currently selling mobile mapping systems; including vehicle, human and robot-borne devices. Through manufacturer participation, this list becomes a data set, which is then assessed and condensed into aspects relevant to focused industries, departments or initiatives that stand to benefit directly from these instruments in their day-to-day operations. This will save hours of internet searching and social media cruising which, ultimately, will not provide such a comprehensive list.
2) Encourage
Encourage discussion in and adoption of SLAM systems
Knowing and candidly showing how the technology performs today gives innovators a ‘tool box’ of potential solutions with which to explore options. Innovators exist everywhere and the CanSLAM wants to provide them with a spark of inspiration and the accelerant needed to keep the pace of change in step with our need for information. What you couldn’t find in one device might be standard kit in another; what projects aren’t suited to any one device may be perfectly suited for a ‘combined arms’ approach of different scanners. Discussion breeds awareness which fosters innovation.
3) Empower
Empower future consumers & users through consideration
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Two of the largest pitfalls of adoption are the inexperience of using revolutionary systems and marketing impressions that cause unrealistic expectations of performance. The cost of these units is already extremely prohibitive, but cost of learning can be even more-so. Knowing what considerations to make when selecting a system and its application could be the difference between resounding success and a regrettably poor investment.
4) Provide
Provide free base data sets for your consideration
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Those looking to buy mobile mapping systems need candid samples of performance to gage suitability of aspects which the CanSLAM may not have directly considered. Providing a library of information allows growth to continue while limiting perception bias.
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The data produced by these instruments is priceless to those who wish to know, learn, and improve not only mobile mapping platforms and SLAM algorithms, but also for artificial intelligence extraction of the data it captures. As such, the computer vision and deep learning disciplines will be encouraged to make use of these datasets to help push automation into a more effective role in geospatial applications.
5) Congregate
Bring manufacturers together
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While systems today are incredible in performance, they continue to develop and grow with unique features or functionality defining each! This goal is established to help form a community with the SLAM and mobile mapping spheres to help accelerate evolution through awareness, communication and, hopefully, collaboration.
6) Integrate
Promote downstream A.I. Integration
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Downstream service providers need samples of unit data test and refine the application of their solution against a post-processed final scan result; artificial intelligence developers need examples of everyday items to train computers what to look for. Mobile mapping and SLAM have taken hours long processes and condensed them to minutes; the next natural step is to take hours of human data extraction and convert it into minutes of A.I. extraction review.
However, open libraries for this type of development are not common. The CanSLAM aims to fill this need with accessible data so that major and minor developers alike have equal opportunity to keep the flame of innovation fed.
Despite simple goals, this undertaking requires participation from multiple stakeholders across education and industry. To this end, we have begun to integrate a network of post-secondary institutions, professional community initiatives, social influencers, and engaged industry members together to maximize industry participation, validate results, foster learning and innovation, and create an unbias resource for you to investigate potential solutions for your own needs and initiatives.
This event simultaneously opens up opportunity to apply this technology where it could be implemented and prove viability of application to situations and scenarios where the technology may be considered 'out of reach,' ineffective, or frivolous in application without affecting real-world budgets, timelines, or project qualities.
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