ABSTRACT:
Simultaneous localization and mapping (SLAM) is the process of constructing a global model from local observations, acquired as a mobile robot moves through an environment. SLAM is a foundational capability for mobile robots, supporting such core functions as planning, navigation, and control, for a wide range of application domains. SLAM is one of the most deeply investigated fields in mobile robotics research, yet many open questions remain to enable the realization of robust, long-term autonomy. This talk will review the historical development of SLAM and will describe several current research projects in our group. Two key themes are increasing the expressive capacity of the environmental models used in SLAM systems (representation) and improving the performance of the algorithms used to estimate these models from data (inference). Our ultimate goal is to provide autonomous robots with a more comprehensive understanding of the world, facilitating life-long learning in complex dynamic environments.
About the Johns Hopkins Institute for Assured Autonomy: Led by APL and the Whiting School of Engineering, the IAA is becoming a nationally recognized center of excellence in autonomous systems, showcasing the robust portfolio of research and work from two premier divisions of JHU and creating strategic external partnerships. The IAA seeks to ensure the safe, secure, and reliable integration of autonomous systems and artificial intelligence (AI) in society. As autonomous systems proliferate, both physically and virtually, the institute seeks to ensure the systems will be trusted and safe in their operations, will withstand corruption by adversaries, and will integrate seamlessly into ecosystems and communities. In this burgeoning field, JHU strives to advance a clear vision for an autonomous future.
Негізгі бет Ғылым және технология John Leonard, "The Past, Present and Future of SLAM" | Johns Hopkins Institute for Assured Autonomy
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