Observable 2D SLAM and Evidential Occupancy Grids

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Creator: 

Radhakrishnan, Sindhu

Date: 

2014

Abstract: 

The two main challenges offered by Simultaneous Localization and Mapping (SLAM)
are that of observability and extending state estimation to exploration. This thesis
explores and uses solutions to render the SLAM problem observable, by proposing the
Reconfigurable Extended Kalman Filter (EKF) that addresses imposing observability,
maintaining observability and choice of observability constraints. Additionally,
Bayesian theory and Dempster-Shafer theory of evidential reasoning are analyzed, and
Occupancy grid based maps based on Dempster-Shafer theory of evidential reasoning
are created
and analyzed in large environment for their potential use in exploration
and obstacle avoidance. Tackling both issues with different algorithms yield better
solutions to the challenges offered by robotic exploration, and this is demonstrated
through simulation results in representative environments.

Subject: 

Robotics
Engineering - Electronics and Electrical

Language: 

English

Publisher: 

Carleton University

Thesis Degree Name: 

Master of Applied Science: 
M.App.Sc.

Thesis Degree Level: 

Master's

Thesis Degree Discipline: 

Engineering, Electrical and Computer

Parent Collection: 

Theses and Dissertations

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