Several weeks ago , I had the pleasure of attending another lecture organized by the Robotics Initiative at Georgia Tech . This time the speaker was Dr. John Leonard who presented his current research in Simultaneous Localization And Mapping (SLAM). Dr. Leonard is a member of MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL). His lecture was an amalgamate of theory, research applications, and future aspirations.

Dr. Leonard concisely describes SLAM by saying on his website:

The problem of SLAM is stated as follows: starting from an initial position, a mobile robot travels through a sequence of positions and obtains a set of sensor measurements at each position. The goal is for the mobile robot to process the sensor data to produce an estimate of its position while concurrently building a map of the environment.

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Last week, Dr. Steven M. LaValle, a Professor of Computer Science at The University of Illinois at Urbana-Champaign, presented as part of Georgia Tech’s Robotics and Intelligent Machines (RIM) Seminar Series. This was the second is a series of eight seminars spanning the Fall 2006 semester and I had the pleasure of attending and hear him speak.

Dr. LaValle structured his lecture around his new book, Planning Algorithms, which is available for free download here. He describes the book by saying:

This book presents a unified treatment of many different kinds of planning algorithms. The subject lies at the crossroads between robotics, control theory, artificial intelligence, algorithms, and computer graphics. The particular subjects covered include motion planning, discrete planning, planning under uncertainty, sensor-based planning, visibility, decision-theoretic planning, game theory, information spaces, reinforcement learning, nonlinear systems, trajectory planning, nonholonomic planning, and kinodynamic planning.

While he was there to present his entire book, he chose to focus on three areas of overlap between mechanical engineering, electrical engineering, and computer science which present “great potential for impact”: motion planning with differential constraints, feedback motion planning, and information spaces.
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