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ECOVACS ROBOTICS' AI Research Paper Presented at IROS 2019

  • Written by ECOVACS ROBOTICS

MACAU, CHINA - Media OutReach[1] - 14 November 2019 - ECOVACS ROBOTICS presented a paper on its 3D Simultaneous Localization and Mapping (SLAM) in service robotics technology at the IROS 2019 conference. In this paper, researchers at the AI Institute (Nanjing, China) of ECOVACS ROBOTICS detailed their research results on an application of semi-supervised learning algorithms based on deep learning for 3D SLAM in the company's household service robots. The paper is also the industry's first research publication relevant to this technology.

ECOVACS ROBOTICS' AI Research Paper Presented at IROS 2019

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ECOVACS ROBOTICS was also the only company at IROS invited to share its AI research and cutting-edge technology with universities and research institutions including: the University of Maryland, the U.S. Military Academy at West Point, and the University of Surrey in a "Thought Leadership" series of documentaries produced by IROS TV.

 

The 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2019) is the flagship international conference in robotics and intelligent systems, which was held in Macau from November 4 to 8. The conference is co-sponsored by the IEEE, the IEEE Robotics and Automation Society (RAS), the Robotics Society of Japan (RSJ), and other prestigious technology associations.

Currently, 2D based VSLAM and LDS SLAM are the two most common indoor mapping and navigation technologies and ECOVACS ROBOTICS is the only home service robotics company providing robots with both VSLAM (visual-based navigation) and LDS SLAM (laser-based navigation) technologies. However, traditional VSLAM technology can be time consuming since it relies on online optimization to provide iterative solutions and localization. Thus, deep learning has been introduced to replace online optimization with the more efficient offline training. After the training, the robot uses the deep network for localization and relatively shortens the time needed for optimization. But most of the deep learning requires supervised learning with vast amounts of labelled data. The semi-supervised learning technology proposed by ECOVACS ROBOTICS therefore solves this problem to a certain extent. The company's SLAM application can improve localization accuracy in a complex environment, enhance the performance of robots, boosts the value for users, and truly frees your hands.

 

The AI Institute (Nanjing), ECOVACS ROBOTICS, was established in July 2018 with a focus on conducting cutting-edge AI research and development, to enable the company to develop innovative new intelligent robotic solutions. In 2019, a R&D team of nearly 30 people set up by the institute successfully completed the initial construction of a high-performance GPU computing platform, distributed storage platform and mobile robot experimental platform. The team also carried out a series of researches on 3D visual perception, tactile perception, behavior recognition, and human-computer interaction to pave the way for ECOVACS ROBOTICS' next generation of robots.

 

According to ECOVACS ROBOTICS' 2019 Mid-Year Financial Report, in mainland China, the company's robotic vacuum cleaners accounted for as much as 48% of total market share. ECOVACS ROBOTICS led the industry in the China market in growing retail sales.  The company's core competitive advantage lies in its continuous, aggressive investment in R&D. ECOVACS ROBOTICS is committed to developing machine vision in new sensor modules, software algorithms and artificial intelligence technologies. At the same time, the company is developing cutting edge technology that will take robotic vacuum cleaners to the next level -- as a technology platform that is more fine-tuned, data-intensive, diversified, and enabling breakthroughs in interaction, intelligence and interconnection.

References

  1. ^ Media OutReach (www.media-outreach.com)

Authors: ECOVACS ROBOTICS

Read more http://www.media-outreach.com/release.php/View/20481#Contact

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