An Evolutionary Algorithm for Depth Image Based Camera Pose Estimation in Indoor Environments
We consider the task of determining the pose of a depth camera based on a single target depth image and a 3D model of the indoor environment that the image was taken in. We identify the quality of a pose estimate with summed differences between depth values in the target depth image and a depth image generated synthetically by using that pose estimate in the 3D model. We then propose an evolutionary algorithm for optimizing pose estimates. In this thesis, we discuss indoor positioning approaches, introduce our evolutionary algorithm, and then evaluate the performance of that algorithm in three artificial test environments. Finally, we discuss the perspectives for the use of the algorithm in real environments.