Probabilistic Point Cloud Modeling via Self-Organizing Gaussian Mixture Models

1 code implementation • 31 Jan 2023 • Kshitij Goel, Nathan Michael, Wennie Tabib

This letter presents a continuous probabilistic modeling methodology for spatial point cloud data using finite Gaussian Mixture Models (GMMs) where the number of components are adapted based on the scene complexity.

Fast Monte-Carlo Localization on Aerial Vehicles using Approximate Continuous Belief Representations

no code implementations • CVPR 2018 • Aditya Dhawale, Kumar Shaurya Shankar, Nathan Michael

Size, weight, and power constrained platforms impose constraints on computational resources that introduce unique challenges in implementing localization algorithms.

Robotics