Keynote on SLAM announced for UKIVA Machine Vision Conference
Dr Luca Benedetti from Kudan will be delivering a keynote address entitled: ‘Visual SLAM in the Wild’ at the UKIVA Machine Vision Conference and Exhibition. The event takes place at the Marshall Arena, Milton Keynes, UK on Thursday 6th June 2019.
With Simultaneous Localisation and Mapping (SLAM) gaining traction in the world of industrial vision, Dr. Benedetti will describe the real-world challenges of deploying a SLAM system across a variety of applications as well as designing a SLAM system that's versatile both in terms of hardware and software.
UKIVA Chairman, Allan Anderson said: “We are delighted that Dr. Benedetti has agreed to speak at the Conference. Kudan is accelerating the evolution of virtuality (AR/MR/VR) and Robotics (automobile/drone/robot) by developing computer software algorithms classified as Artificial Perception (AP). They are pioneers in this field. With the combination of AP and AI, machines are getting closer to sensing and interacting with the world like humans do, utilising both eyes and brain.”
SLAM refers to the process of determining the position and orientation (localisation) of a sensor with respect to its surroundings, as well as simultaneously building a map of the surrounding environment. Most modern SLAM systems used today are based on vision, as they use one or more cameras as the main sensing device and visual SLAM is quickly becoming an important advancement in embedded vision with many potential applications.
The Machine Vision Conference program features around 60 technical seminars across eight different subject areas: Vision in Robotics, Understanding Vision Technology, Deep Learning & Embedded Vision, 3D Vision, Optics & Illumination, Camera Technology, Systems & Applications and Vision Innovation. The Exhibition features a world-class cross section of companies that serve the machine vision industry. It will provide visitors with the opportunity to see some of the latest vision products and talk to experts about any aspect of machine vision.