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A Probabilistic Framework for Real-Time Mapping on an Unmanned Ground Vehicle


Gillula, Jeremy Hugh (2006) A Probabilistic Framework for Real-Time Mapping on an Unmanned Ground Vehicle. Senior thesis (Minor), California Institute of Technology. doi:10.7907/SXNY-XG55.


In the course of preparing for the 2005 DARPA Grand Challenge, an off-road race for autonomous vehicles, a group of undergraduates from Caltech developed a set of deterministic algorithms for performing sensor fusion on maps generated by different range sensors on a mobile robot. That framework had serious limitations, however, including "disappearing" obstacles and lack of confidence data associated with features in the maps. In this thesis, we present a probabilistic framework that attempts to solve some of these problems by using error models of two typical types of range sensors, as well as by making use of Kalman filtering techniques from control theory to fuse the resulting measurements into an accurate digital elevation map. Our results indicate that this probabilistic framework has several advantages over the determinisic framework used by Team Caltech in the 2005 Grand Challenge.

Item Type:Thesis (Senior thesis (Minor))
Subject Keywords:DGC, DARPA Grand Challenge, Team Caltech, Alice, probabilistic robotics, sensor fusion
Degree Grantor:California Institute of Technology
Division:Engineering and Applied Science
Major Option:Computer Science
Minor Option:Control and Dynamical Systems
Thesis Availability:Public (worldwide access)
Research Advisor(s):
  • Burdick, Joel Wakeman (advisor)
  • Murray, Richard M. (co-advisor)
  • Perona, Pietro (co-advisor)
Thesis Committee:
  • None, None
Defense Date:8 June 2006
Record Number:CaltechThesis:01112018-111923389
Persistent URL:
Gillula, Jeremy Hugh0000-0003-1800-8222
Default Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:10641
Deposited By: Kathy Johnson
Deposited On:11 Jan 2018 19:23
Last Modified:08 Nov 2023 00:44

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