2026-09-20T02:00:07Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1242482026-06-06T00:55:09Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Sertac Karaman and Vivienne Sze.
Henderson, Trevor(Trevor F.)
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2020-03-24T15:36:19Z
2020-03-24T15:36:19Z
2019
2019
https://hdl.handle.net/1721.1/124248
1145119356
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 79-82).
In this thesis we derive an algorithm that addresses the computational bottleneck of robotic exploration: computing the expected information gain -- i.e. mutual information -- between an occupancy map and a range sensor measurement. The algorithm we derive has a lower complexity and in practice runs 200 to 1500 times faster than the state of the art CSQMI and FSMI algorithms. The speedup is due to the realization that the mutual information at one cell of an occupancy map can be defined in terms of the mutual information at adjacent cells. This makes computing the mutual information at all cells in the map much faster than computing the mutual information of each cell independently. The derivation is unique in that it models the occupancy map and range measurements as continuous random fields despite the fact that actual computation requires quantization. This framework is critical to the recursive definitions that provide performance gain. It also reveals flaws, previously obscured by discretization, in several well established concepts: the practice of initializing occupancy probabilities in an occupancy grid to 1/2 is arbitrary and in application often an overestimate; and the formula for mutual information defined by Julian et al. fails to take into account a radial volume element, which changes mutual information values dramatically. Both of these claims are supported empirically. Finally, we investigate two heuristics that use mutual information computation to perform actual exploration tasks and provide an analysis of each heuristic's use case. These claims are validated by synthetic exploration experiments.
by Trevor Henderson.
M.Eng.
M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
82 pages
eng
Massachusetts Institute of Technology
MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
A continuous approach to information-theoretic exploration with range sensors
Thesis
application/pdf
2020-03-24T15:36:18Z
Publication
Master
EECS
unknown
unknown
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