Autonomous robotic mapping has been an open research topic for more than twenty years. The primary objective of the robotic mapping problem is to design methods that can guide a robot around an environment and allow it to create a map of what has been sensed. Most automatic mapping algorithms rely on robot pose estimation to fuse map data together. This paper demonstrates that through feature extraction using spin-histograms, the pose of the robot can be estimated accurately enough for an iterative closest point (ICP) algorithm to register overlapping data sets. By eliminating consideration for points according to curvature and saliency, the spin-histogram matching process can improve in both accuracy and computation time. In combination with a global registration algorithm known as simultaneous matching, this process can obtain a fully autonomous registration process.

This paper presents a method to improve the accuracy rates of face authentication between images with different poses. Trilinear tensors are used to adjust the pose of the training and testing images. All the images are transformed by a pose adjustment algorithm so novel images are generated that have the same pose. These novel images are then used to train and test support vector machine (SVM) face authentication functions to verify the identity of the people in the images. The overall results show that the accuracy improves when the poses of the images are adjusted.