Investigation into the use of the Microsoft Kinect and the Hough transform for mobile robotics

Abstract

The Microsoft Kinect sensor is a low cost RGB-D sensor. In this dissertation, its calibration

is fully investigated and then these parameters are compared to the parameters

given by Microsoft and OpenNI. The parameters found were found to be di_erent to

those given by Microsoft and OpenNI therefore, every Kinect should be fully calibrated.

The transformation from the raw data to a point cloud is also investigated.

Then, the Hough transform is presented in its 2-dimensional form. The Hough transform

is a line extraction algorithm which uses a voting system. It is then compared to

the Split-and-Merge algorithm using laser range _nder data. The Hough transform is

found to compare well to the Split-and-Merge in 2 dimensions.

Finally, the Hough transform is extended into 3-dimensions for use with the Kinect

sensor. It was found that pre-processing of the Kinect data was necessary to reduce the

number of points input into the Hough transform. Three edge detectors are used - the

LoG, Canny and Sobel edge detectors. These were compared, and the Sobel detector

was found to be the best. The _nal process was then used in multiple ways - _rst to

determine its speed. Its accuracy was then investigated. It was found that the planes

extracted were very inaccurate, and therefore not suitable for obstacle avoidance in

mobile robotics. The suitability of the process for SLAM was also investigated. It was

found to be unsuitable, as planar environments did not have distinct features which

could be tracked, whilst the complex environment was not planar, and therefore the

Hough transform would not work.