Safe and Efficient Estimation for Robotics through the Optimal Use of Resources

TL;DR

This paper proposes a comprehensive approach to robotic state estimation that optimally utilizes multiple sensor modalities, including RF and sound, through certifiably optimal algorithms and data-driven models to improve accuracy and efficiency.

Contribution

It introduces a multi-modality framework combining RF and audio sensors with certifiably optimal solvers and data-driven models for enhanced robotic state estimation.

Findings

Effective integration of RF and audio sensors improves estimation accuracy.

Certifiably optimal algorithms outperform local solvers in non-convex problems.

Data-driven models enhance the adaptability and robustness of the estimation process.

Abstract

In order to operate in and interact with the physical world, robots need to have estimates of the current and future state of the environment. We thus equip robots with sensors and build models and algorithms that, given some measurements, produce estimates of the current or future states. Environments can be unpredictable and sensors are not perfect. Therefore, it is important to both use all information available, and to do so optimally: making sure that we get the best possible answer from the amount of information we have. However, in prevalent research, uncommon sensors, such as sound or radio-frequency signals, are commonly ignored for state estimation; and the most popular solvers employed to produce state estimates are only of local nature, meaning they may produce suboptimal estimates for the typically non-convex estimation problems. My research aims to use resources more optimally, by building on 1) multi-modality: using ubiquitous RF transceivers and microphones to support state estimation, 2) building certifiably optimal solvers and 3) learning and improving adequate models from data.