An Efficient and Continuous Approach to Information-Theoretic Exploration
Name
2020_icra_fcmi.pdf
Description
Accepted version
Size
1.22 MB
Format
Adobe PDF
Checksum (MD5)
db5607856e5e2489c4c4eb64650ff57a
Author(s)
Henderson, Theia
Sze, Vivienne
Karaman, Sertac
Date Issued
September 2020
Journal
2020 IEEE International Conference on Robotics and Automation
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Henderson, Theia et al. "An Efficient and Continuous Approach to Information-Theoretic Exploration." 2020 IEEE International Conference on Robotics and Automation, May-August 2020, virtual event (Paris, France), Institute of Electrical and Electronics Engineers, September 2020. © 2020 IEEE
Version
Author's final manuscript
Abstract
Exploration of unknown environments is embedded and essential in many robotics applications. Traditional algorithms, that decide where to explore by computing the expected information gain of an incomplete map from future sensor measurements, are limited to very powerful computational platforms. In this paper, we describe a novel approach for computing this expected information gain efficiently, as principally derived via mutual information. The key idea behind the proposed approach is a continuous occupancy map framework and the recursive structure it reveals. This structure makes it possible to compute the expected information gain of sensor measurements across an entire map much faster than computing each measurements' expected gain independently. Specifically, for an occupancy map composed of |M| cells and a range sensor that emits |Θ| measurement beams, the algorithm (titled FCMI) computes the information gain corresponding to measurements made at each cell in O(|Θ||M|) steps. To the best of our knowledge, this complexity bound is better than all existing methods for computing information gain. In our experiments, we observe that this novel, continuous approach is two orders of magnitude faster than the state-of-the-art FSMI algorithm.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Microsystems Technology Laboratories
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/icra40945.2020.9196592
