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Massachusetts Institute of Technology

Sparsity and computation reduction for high-rate visual-inertial odometry

Abstract

dc:description.abstract

The navigation problem for mobile robots operating in unknown environments can be posed as a subset of Simultaneous Localization and Mapping (SLAM). For computationally-constrained systems, maintaining and promoting system sparsity is key to achieving the high-rate solutions required for agile trajectory tracking. This thesis focuses on the computation involved in the elimination step of optimization, showing it to be a function of the corresponding graph structure. This observation directly motivates the search for measurement selection techniques to promote sparse structure and reduce computation. While many sophisticated selection techniques exist in the literature, relatively little attention has been paid to the simple yet ubiquitous heuristic of decimation. This thesis shows that decimation produces graphs with an inherently sparse, partitioned super-structure. Furthermore, it is shown analytically for single-landmark graphs that the even spacing of observations characteristic of decimation is near optimal in a weighted number of spanning trees sense. Recent results in the SLAM community suggest that maximizing this connectivity metric corresponds to good information-theoretic performance. Simulation results confirm that decimation-style strategies perform as well or better than sophisticated policies which require significant computation to execute. Given that decimation consumes negligible computation to evaluate, its performance demonstrated here makes decimation a formidable measurement selection strategy for high-rate, realtime SLAM solutions. Finally, the SAMWISE visual-inertial estimator is described, and thorough experimental results demonstrate its robustness in a variety of scenarios, particularly to the challenges prescribed by the DARPA Fast Lightweight Autonomy program.

Degree

thesis:*
Department dc:contributor.department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics.
Grantor dc:publisher
Massachusetts Institute of Technology
Year dc:date.issued
2017

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Frey, Kristoffer M. (Kristoffer Martin)
Advisor dc:contributor.advisor
  • Jonathan P. How and Theodore J. Steiner.

Subjects

dc:subject × 1

Rights

dc:rights
Statement dc:rights
  • 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.
Language dc:language.iso
eng

Identifiers

dc:identifier.*
Handle dc:identifier.uri
http://hdl.handle.net/1721.1/113745
OAI identifier oai:identifier
oai:dspace.mit.edu:1721.1/113745

Chain of custody

source
Harvested from
MIT
Base URL
dspace.mit.edu/oai/request
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
citation

Frey, Kristoffer M. (Kristoffer Martin). Sparsity and computation reduction for high-rate visual-inertial odometry. Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/113745