Dynamic RGB-D Mapping

Localization and mapping has been an area of great importance and interest to the robotics and computer vision community. It has traditionally been accomplished with range sensors such as lasers and sonars. Recent improvements in processing power coupled with advancements in image matching and motion estimation has allowed development of vision based localization techniques. Despite much progress, there are disadvantages to both range sensing and vision techniques making localization and mapping that is inexpensive and robust hard to attain. With the advent of RGB-D cameras which provide synchronized range and video data, localization and mapping is now able to exploit both range data as well as RGB features. This thesis exploits the strengths of vision and range sensing localization and mapping strategies and proposes novel algorithms using RGB-D cameras. We show how to combine existing strategies and present through evaluation of the resulting algorithms against a dataset of RGB-D benchmarks. Lastly we demonstrate the proposed algorithm on a challenging indoor dataset and demonstrate improvements where either pure range sensing or vision techniques perform poorly.