The state of the art in SLAM and why FPGA-SoCs can play an important role

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• Konstantinos Boikos (Imperial College)
• Monday 29 January 2018, 16:00-17:00
• Level 9 Seminar Room, EE Dept..

If you have a question about this talk, please contact George A Constantinides.

Simultaneous Localization and Mapping (SLAM) is a field which witnessed an explosion of new ideas and directions in the last decade, and now with emerging applications such as self-driving cars, unmanned autonomous vehicles (aerial and ground) and augmented reality gaining ground, it has become more relevant than ever. Traditionally larger robotic systems such as self-driving cars have been relying on a sensor fusion with active Laser or Lidar systems collaborating with sonar and other sensors to perform self-localization and obstacle avoidance. Recently, camera-based SLAM and odometry has seen a significant boost in accuracy and performance, and although such passive sensor-based algorithms require certain conditions to produce high quality results, they offer several advantages in weight, size, power consumption and potentially higher resolution and range as well. This makes them very attractive for a lot of applications and as a result there is a need for accurate and robust visual SLAM in embedded systems. In this talk we will provide an overview of the recent advances in the field of SLAM and its embedded applications in the state of the art, as well as the state of our current work and where it fits in this area.

This talk is part of the CAS Talks series.

This talk is included in these lists:

• Andrea Picciau's list
• CAS Talks
• Circuits and Systems Group: Internal Seminars
• Level 9 Seminar Room, EE Dept.

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