MIMOSA: A Multi-Modal SLAM Framework for Resilient Autonomy against Sensor Degradation
Nikhil Khedekar, Mihir Kulkarni, Kostas Alexis
Abstract
This paper presents a framework for Multi-Modal SLAM (MIMOSA) that utilizes a nonlinear factor graph as the underlying representation to provide loosely-coupled fusion of any number of sensing modalities. Tailored to the goal of enabling resilient robotic autonomy in GPS-denied and perceptually-degraded environments, MIMOSA currently contains modules for pointcloud registration, fusion of multiple odometry estimates relying on visible-light and thermal vision, as well as inertial measurement propagation. A flexible back-end utilizes the estimates from various modalities as relative transformation factors. The method is designed to be robust to degeneracy through the maintenance and tracking of modality-specific health metrics, while also being inherently tolerant to sensor failure. We detail this framework alongside our implementation for handling high-rate asynchronous sensor measurements and evaluate its performance on data from autonomous subterranean robotic exploration missions using legged and aerial robots.
BibTeX
@inproceedings{iros2022_mimosaamultimoda,
title = {MIMOSA: A Multi-Modal SLAM Framework for Resilient Autonomy against Sensor Degradation},
author = {Nikhil Khedekar and Mihir Kulkarni and Kostas Alexis},
booktitle = {IROS 2022},
year = {2022}
}