Andrea De Maio

2 accepted papers

2022

Deep Bayesian ICP Covariance Estimation

ICRA 2022poster

Covariance estimation for the Iterative Closest Point (ICP) point cloud registration algorithm is essential for state estimation and sensor fusion purposes. We argue that a major source of error for ICP is in the input data itself, from the sensor noise to the scene geometry. Benefiting from recent…

Cited by 12 Source ↗Scholar ↗

2020

Simultaneously Learning Corrections and Error Models for Geometry-Based Visual Odometry Methods

Andrea De Maio, Simon Lacroix

RA-L 2020

This letter fosters the idea that deep learning methods can be used to complement classical visual odometry pipelines to improve their accuracy and to associate uncertainty models to their estimations. We show that the biases inherent to the visual odometry process can be faithfully learned and comp

Cited by 9 Source ↗Scholar ↗