<p>Buildings, maps and objects et cetera, can be modeled using a computer or reconstructed in 3D by data from different kinds of cameras or laser scanners. This thesis concerns the latter. The recent improvements of Time-of-Flight cameras have brought a number of new interesting research areas to the surface. Registration of several ToF camera point clouds is such an area.</p><p>A literature study has been made to summarize the research done in the area over the last two decades. The most popular method for registering point clouds, namely the Iterative Closest Point (ICP), has been studied. In addition to this, an error relaxation algorithm was implemented to minimize the accumulated error of the sequential pairwise ICP.</p><p>A few different real-world test scenarios and one scenario with synthetic data were constructed. These data sets were registered with varying outcome. The obtained camera poses from the sequential ICP were improved by loop closing and error relaxation.</p><p>The results illustrate the importance of having good initial guesses on the relative transformations to obtain a correct model. Furthermore the strengths and weaknesses of the sequential ICP and the utilized error relaxation method are shown.</p>

Buildings, maps and objects et cetera, can be modeled using a computer or reconstructed in 3D by data from different kinds of cameras or laser scanners. This thesis concerns the latter. The recent improvements of Time-of-Flight cameras have brought a number of new interesting research areas to the surface. Registration of several ToF camera point clouds is such an area. A literature study has been made to summarize the research done in the area over the last two decades. The most popular method for registering point clouds, namely the Iterative Closest Point (ICP), has been studied. In addition to this, an error relaxation algorithm was implemented to minimize the accumulated error of the sequential pairwise ICP. A few different real-world test scenarios and one scenario with synthetic data were constructed. These data sets were registered with varying outcome. The obtained camera poses from the sequential ICP were improved by loop closing and error relaxation. The results illustrate the importance of having good initial guesses on the relative transformations to obtain a correct model. Furthermore the strengths and weaknesses of the sequential ICP and the utilized error relaxation method are shown.

Three dimensional mapping is becoming an increasingly attractive product nowadays. Many devices like laser scanner or stereo systems provide 3D scene reconstruction. A new type of active sensor, the Time of Flight (ToF) camera obtains direct depth observations (3rd dimensional coordinate) in a high video rate, useful for interactive robotic and navigation applications. The high frame rate combined with the low weight and the compact design of the ToF cameras constitute an alternative solution of the 3D measuring technology. However a deep understanding of the error involved in the ToF camera observations is essential in order to upgrade their accuracy and enhance the ToF camera performance. This thesis work addresses the depth error characteristics of the SR4000 ToF camera and indicates potential error models for compensating the impact. In the beginning of the work the thesis investigates the error sources, their characteristics and how they influence the depth measurements. In the practical part, the work covers the above analysis via experiments. Last, the work proposes simple methods in order to reduce the depth error so that the ToF camera can be used for high accuracy applications.   An overall result of the work indicates that the depth acquired by the Time of Flight (ToF) camera deviates several centimeters, specifically the SR4000 camera provides 35 cm error size for the working range of 1-8 m. After the error compensation the depth offset fluctuates 15cm within the same working range. The error is smaller when the camera is set up close to the test field than when it is further away.

Thesis deals with automatic guided mobile robot focused on obstacle avoidance during ride on planned route. There are summaries of usually used obstacle detecting sensors and algorithms used for path finding. Based on this, own solution is designed. It uses waypoints changes to pass obstacle. MATLAB simulation is created for tests of new designed method. This method is implemented to real robot for real world testing. Reached goals and upgrade possibilities are summarized in bottom of thesis.

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