CMU RI Thesis Proposal: Integrated Localization, Mapping, and Planning in Unstructured 3D Environments

Nathaniel Fairfield (than@cmu.edu)
Robotics Institute
Carnegie Mellon University

Abstract:

The ability to explore an unknown environment is a prerequisite for most useful mobile robotics. Exploration can be decomposed into the tasks of perceiving the environment to build a map, localizing within that map, and planning where to explore next. Over the past ten years or so, the field of simultaneous localization and mapping (SLAM) has been active and increasingly applied. More recently, work has been directed towards the problem of planning as an integral part of exploration and SLAM. Another persistent challenge is scale many SLAM formulations have problems with exploring areas. We are interested in developing an integrated mapping, localization, and planning approach that can handle large scale three-dimensional environments and sparse sensor data. As a start, we have developed a method for doing SLAM using a Rao-Blackwellized Particle Filter and evidence grid-based maps, and demonstrated successful SLAM using an autonomous underwater vehicle in a 3D environment. The two major limitations of our current method are its inability to scale the evidence grid approach to truly large environments (hundreds of meters and millions of observations), and its lack of planning ability for picking exploration and/or uncertainty-reducing actions. We propose to address the first limitation by developing SLAM on multiple scales: local submaps and global maps; in effect using the submaps as features at larger scale. We propose to address the second limitation, planning, by integrating the tasks of mapping, localizing, and planning under an information-theoretic framework. The planning algorithm will use models of unmapped regions and the entropy of the predicted SLAM state to choose the action with the greatest estimated information gain. The combination of multi-scale SLAM and information gain-based planning raises the possibility of hierarchical exploration, where the robot's current task determines its exploration strategy. In this proposal we describe our current work, motivation, and proposed solutions, with the goal of building a system which is capable of exploring large-scale 3D environments.

Further Details: http://gs4435.sp.cs.cmu.edu/fairfield_proposal.pdf