DP-SLAM: Mapping Dense Environments with Speed and Precision

Simultaneous Localization and Mapping (SLAM) is the task of building an accurate map of an environment without getting lost in the process. This problem is of great significance in robotics for situations in which an accurate global position sensor, such as GPS, is not available. This includes undersea, subterranean, and space exploration missions, as well as most indoor environments.

A major challenge faced by SLAM algorithms is that of avoiding accumulating error: Small errors in localization can lead to small errors in the map which, when compounded over a long exploration path, can lead to inconsistent and misaligned maps. I will present the DP-SLAM algorithm, an approach to the slam problem that minimizes accumulated error by efficiently maintaining hundreds of map hypotheses using a particle filter and a novel map data structure.

Using DP-SLAM, we have built maps at 3cm resolution with no discernible alignment errors or blemishes for robot trajectories over 100m. Our approach can handle highly ambiguous environments with features such as glass and thin columns.

The web site for the project, which includes sample maps, is: http://www.cs.duke.edu/~parr/dpslam.

This talk is based on joint work with Austin Eliazar (Duke University).

Speaker Bio

Since 2000, Ron Parr has been assistant professor at the Duke University Computer Science Department. He received his A.B. (Cum Laude) in Philosophy in 1990 from Princeton University, where he was advised by Gilbert Harman. His bachelor's thesis, "Minds, Brains and Searle," addressed Searle's criticisms of strong AI. In 1998, he received his Ph.D. in computer science from the University of California at Berkeley, under the supervision of Stuart Russell. His dissertation topic was, "Hierarchical Control and Learning for Markov Decision Processes." After graduating from Berkeley, Ron spent two years as a postdoctoral research associate at Stanford University, where he worked with Daphne Koller. At Stanford, Ron worked on decision theory, tracking and on solving factored Markov Decision Processes using linear value function approximators. Ron's current research interests include most forms of planning under uncertainty, reinforcement learning and robotics. Ron has served on the editorial board of the Journal of Artificial Intelligence Research (JAIR) and was selected as a Sloan fellow in 2003.