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Tools and Algorithms for Mobile Robot Navigation with Uncertain Localization


Kriechbaum, Kristopher Lars (2006) Tools and Algorithms for Mobile Robot Navigation with Uncertain Localization. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/R6YB-NQ21.


The ability for a mobile robot to localize itself is a basic requirement for reliable long range autonomous navigation. This thesis introduces new tools and algorithms to aid in robot localization and navigation. I introduce a new range scan matching method that incorporates realistic sensor noise models. This method can be thought of as an improved form of odometry. Results show an order of magnitude of improvement over typical mobile robot odometry. In addition, I have created a new sensor-based planning algorithm where the robot follows the locally optimal path to the goal without exception, regardless of whether or not the path moves towards or temporarily away from the goal. The cost of a path is defined as the path length. This new algorithm, which I call "Optim-Bug," is complete and correct. Finally, I developed a new on-line motion planning procedure that determines a path to a goal that optimally allows the robot to localize itself at the goal. This algorithm is called "Uncertain Bug." In particular, the covariance of the robot's pose estimate at the goal is minimized. This characteristic increases the likelihood that the robot will actually be able to reach the desired goal, even when uncertainty corrupts its localization during movement along the path. The robot's path is chosen so that it can use known features in the environment to improve its localization. This thesis is a first step towards bringing the tools of mobile robot localization and mapping together with ideas from sensor-based motion planning.

Item Type:Thesis (Dissertation (Ph.D.))
Subject Keywords:kalman filters; localization; navigation; robotics; sensor-based motion planning; uncertainty
Degree Grantor:California Institute of Technology
Division:Engineering and Applied Science
Major Option:Mechanical Engineering
Thesis Availability:Public (worldwide access)
Research Advisor(s):
  • Burdick, Joel Wakeman
Thesis Committee:
  • Burdick, Joel Wakeman (chair)
  • Collins, Curtis L.
  • Antonsson, Erik K.
  • Hunt, Melany L.
  • Murray, Richard M.
Defense Date:23 May 2006
Non-Caltech Author Email:kristopher.l.kriechbaum (AT)
Record Number:CaltechETD:etd-06012006-150109
Persistent URL:
Default Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:2363
Deposited By: Imported from ETD-db
Deposited On:02 Jun 2006
Last Modified:20 Apr 2020 21:14

Thesis Files

PDF (kriechbaum-thesis.pdf) - Final Version
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