3 accepted papers
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 a…
Size, weight, and power constrained platforms impose constraints on computational resources that introduce unique challenges in implementing localization algorithms. We present a framework to perform fast localization on such platforms enabled by the compressive capabilities of Gaussian Mixture Mode…
In unknown, cluttered environments, robots require online real-time mapping and collision checking in order to navigate robustly. Discrete map representations are inefficient for collision checking as they are expensive in terms of memory and computation. This paper takes a probabilistic approach to…