Localization and orientation system for robotic wireless capsule endoscope

Medical doctors can visualize the gastrointestinal tract through an endoscope. However, existing endoscopes have several limitations such as inaccessibility to the entire intestine and high discomfort. The wireless capsule endoscope, with external guidance for interactive GI tract examination, tries to address some of these limitations. However, the current techniques do not provide good localization of the endoscope capsule. In this thesis, I propose a new wireless robotic capsule endoscope that uses a magnet based localization and orientation system. Here, the capsule, which encloses a permanent magnet, moves in a patient's GI tract, and creates a magnetic field. The field intensities at some spatial points are measured by magnetic sensors placed around the patient's body. We have derived the magnetic intensities as nonlinear functions of magnet's 3D location and 2D orientation parameters, which can be solved by minimizing an objective error function using an optimization algorithm (e.g. Levenberg-Marquardt method). However, the nonlinear method requires a good initial guess of the unknown parameters, and its speed is low. Therefore, I propose a linear algorithm. With the data from five (or more) 3-axis magnetic sensors, this algorithm is realized through matrix computations. In most cases, it results in correct solution if the matrix is not singular, and its execution time is less than one-tenth that of the fastest nonlinear algorithm. Finally, a real sensor system has been built with sixteen Honeywell 3-axis AMR sensors. With a computer interface for graphic and data display, real-time tracking is realized, and the results are satisfactory with an average localization error of about 3.3 mm and an orientation error of about 3.0° in case that the magnet moves within the area of the sensor array. The magnet's orientation misses the information of capsule's rotation along its own central axis, and hence a supplemental method is proposed by an imaging technique. Applying an 8-point algorithm on the images after distortion correction, we solve the 3D rotation and 2D transition parameters of the camera and this rotation can be further decomposed into two sub-rotations, the rotation of the camera's central axis and the rotation around this axis.