Simultaneous Localization And Mapping Using a Kinect in a Sparse Feature Indoor Environment
Localization and mapping are two of the most central tasks when it comes toautonomous robots. It has often been performed using expensive, accurate sensorsbut the fast development of consumer electronics has made similar sensorsavailable at a more affordable price.
In this master thesis a TurtleBot\texttrademark\, robot and a MicrosoftKinect\texttrademark\, camera are used to perform Simultaneous Localization AndMapping, SLAM. The thesis presents modifications to an already existing opensource SLAM algorithm. The original algorithm, based on visual odometry, isextended so that it can also make use of measurements from wheel odometry and asingle axis gyro. Measurements are fused using an Extended Kalman Filter,EKF, operating in a multirate fashion. Both the SLAM algorithm and the EKF areimplemented in C++ using the framework Robot Operating System, ROS.
The implementation is evaluated on two different data sets. One set isrecorded in an ordinary office room which constitutes an environment with manylandmarks. The other set is recorded in a conference room where one of the wallsis flat and white. This gives a partially sparse featured environment.
The result by providing additional sensor information is a more robust algorithm.Periods without credible visual information does not make the algorithm lose itstrack and the algorithm can thus be used in a larger variety of environmentsincluding such where the possibility to extract landmarks is low. The resultalso shows that the visual odometry can cancel out drift introduced bywheel odometry and gyro sensors.
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