Information-based Active SLAM via topological feature graphs
Author(s)
Mu, Beipeng
Giamou, Matthew
Paull, Liam
Leonard, John J
Aghamohammadi, Aliakbar
How, Jonathan P
Date Issued
December 2016
Journal
2016 IEEE 55th Conference on Decision and Control
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Mu, Beipeng et al. "Information-based Active SLAM via topological feature graphs." 2016 IEEE 55th Conference on Decision and Control December 2016, Las Vegas, NV, USA, Institute of Electrical and Electronics Engineers (IEEE), December 2016. © 2016 IEEE
Version
Author's final manuscript
Abstract
Exploring an unknown space and building maps is a fundamental capability for mobile robots. For fully autonomous systems, the robot would further need to actively plan its paths during exploration. The problem of designing robot trajectories to actively explore an unknown environment and minimize the map error is referred to as active simultaneous localization and mapping (active SLAM). Existing work has focused on planning paths with occupancy grid maps, which do not scale well and suffer from long term drift. This work proposes a Topological Feature Graph (TFG) representation that scales well and develops an active SLAM algorithm with it. The TFG uses graphical models, which utilize independences between variables, and enables a unified quantification of exploration and exploitation gains with a single entropy metric. Hence, it facilitates a natural and principled balance between map exploration and refinement. A probabilistic roadmap path-planner is used to generate robot paths in real time. Experimental results demonstrate that the proposed approach achieves better accuracy than a standard grid-map based approach while requiring orders of magnitude less computation and memory resources.
MIT Department
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/cdc.2016.7799127
