Octree-based localization using RGB-D data for indoor robots

Mobile robots need to know their position in the environment to perform complex tasks. Self-localization methods for indoor environments usually reduce sensory information to two dimensions in order to accelerate calculations, instead of taking advantage of the three-dimensional (3D) features. This paper proposes a computationally affordable probabilistic self-localization method that takes full advantage of the capacities of RGB-D sensors, particularly, using 3D dimensions and color. We use an a priori known map, previously built, that keeps the information of the environment's structure and color. Storing this data allows to reduce symmetries caused by 2D and colorless simplification. Our approach is based on Monte Carlo algorithm, where hypothesis population varies dynamically to adapt itself to the existing conditions at any given time, which makes it more efficient in 3D information management.