Environment characterization using Laplace eigenvalues

This paper introduces a new methodology for environment characterization. This methodology is based on analysis of the eigenvalues of Laplace-Beltrami operator over 3 dimensional point clouds. Recognizing revisited places can be facilitated by characterizing the environment through a descriptor. The idea of analyzing point clouds using the eigenvalues of Laplace-Beltrami operator for characterization of an environment can be used for place detection which is a critical functionality of autonomous mobile robots. Place detection is a requirement for transition detection in multi environment missions, common frame identification in multi robot mapping, and detection of previously visited location in SLAM for loop closure phase.