Efficient Parametric Multi-Fidelity Surface Mapping

State-of-the-art dense mapping approaches cannot be deployed on Size, Weight, and Power (SWaP) constrained platforms because of their large memory and compute requirements. In this paper, we present an accurate, and efficient approach to dense multi-fidelity 3D mapping using Gaussian distributions as volumetric primitives. The proposed mapping approach supports both high fidelity dense surface reconstruction and lower fidelity volumetric environment representation for fundamental robotics applications. We exploit the inherent working characteristics of an off-the-shelf depth sensor and approximate the distribution of approximately planar points using Gaussian distributions. Explicit modeling of the sensor noise characteristics enable us to incrementally update the map representation in real-time with high accuracy. We present the advantages of our proposed map representation over other well known state-of-the-art representations by highlighting its superior performance in terms of reconstruction accuracy, completeness and map compression properties via quantitative and qualitative metrics.