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Pose estimation with Sonar

Marck, J.M. (2006) Pose estimation with Sonar. Master's Thesis / Essay, Artificial Intelligence.

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Probabilistiq Robotics is a relatively young approach to robotics. It emphasizes uncertainty in robot perception and action. Using probability theory it is able to represent this uncertainty explicitly. Probability density distributions are used instead of a single "best guess". This way it can model ambiguity and belief in a mathematically sound way. The research for my thesis focuses on pose estimation. Pose estimation is the estimation of location and orientation of the robot. Odometry gives a very good estimation of the current pose, but still gives only an estimation. Over time the cumulative odometry error can grow quite large. This error can be corrected using sensor information. But using only one scan is not always adequate. Matching multiple sensor readings can provide extra information. This is called scan matching. If these two errors are combined with odometric information using a Kalman filter, scan matching should provide an improved pose estimation. Usually scan matching is done with laser range finders. For my implementation I adapted the most widely used scan match algorithm so it can be used with sonar range finders. I tested four models for determining the translational and rotational update in the Kalman Filter. I will give the results to these tests and a general overview of Probabilistic Robotics.

Item Type: Thesis (Master's Thesis / Essay)
Degree programme: Artificial Intelligence
Thesis type: Master's Thesis / Essay
Language: English
Date Deposited: 15 Feb 2018 07:30
Last Modified: 15 Feb 2018 07:30

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