Research

Asynchronous Distributed Smoothing and Mapping via On-Manifold Consensus ADMM

Abstract: In this paper we present a fully distributed, asynchronous, and general purpose optimization algorithm for Consensus Simultaneous Localization and Mapping (CSLAM). Multi-robot teams require that agents have timely and accurate solutions to their state as well as the states of the other robots in the team. To optimize this solution we develop a CSLAM back-end based on Consensus ADMM called MESA (Manifold, Edge-based, Separable ADMM). MESA is fully distributed to tolerate failures of individual robots, asynchronous to tolerate practical network conditions, and general purpose to handle any CSLAM problem formulation. We demonstrate that MESA exhibits superior convergence rates and accuracy compare to existing state-of-the art CSLAM back-end optimizers.

Accepted to ICRA 2024!

Pre-Print: arXiv

Robust Incremental Smoothing and Mapping (riSAM)

Abstract: This paper presents a method for robust optimization for online incremental Simultaneous Localization and Mapping (SLAM). Due to the NP-Hardness of data association in the presence of perceptual aliasing, tractable (approximate) approaches to data association will produce erroneous measurements. We require SLAM back-ends that can converge to accurate solutions in the presence of outlier measurements while meeting online efficiency constraints. Existing robust SLAM methods either remain sensitive to outliers, become increasingly sensitive to initialization, or fail to provide online efficiency. We present the robust incremental Smoothing and Mapping (riSAM) algorithm, a robust back-end optimizer for incremental SLAM based on Graduated Non-Convexity. We demonstrate on benchmarking datasets that our algorithm achieves online efficiency, outperforms existing online approaches, and matches or improves the performance of existing offline methods.

Accepted to ICRA 2023!

Code: github

Pre-Print: arXiv

Publication: IEEE Xplore

Please cite as:

@inproceedings{mcgann_risam_2023, 
    title = {Robust Incremental Smoothing and Mapping ({riSAM})},
    author = {D. McGann and J.G. Rogers III and M. Kaess},
    fullauthor = {Daniel McGann and John G. Rogers III and Michael Kaess},
    year = 2023,
    booktitle = {Proc. IEEE Intl. Conf. on Robotics and Automation (ICRA)},
    address = {London, {GB}}
    pages = {4157-4163},
}

GPS-Denied Global Visual-Inertial Ground Vehicle State Estimation via Image Registration

Abstract: Robotic systems such as unmanned ground vehicles (UGVs) often depend on GPS for navigation in outdoor environments. In GPS-denied environments, one approach to maintain a global state estimate is localizing based on preexisting georeferenced aerial or satellite imagery. However, this is inherently challenged by the significantly differing perspectives between the UGV and reference images. In this paper, we introduce a system for global localization of UGVs in remote, natural environments. We use multi-stereo visual inertial odometry (MSVIO) to provide local tracking. To overcome the challenge of differing viewpoints we use a probabilistic occupancy model to generate synthetic orthographic images from color images taken by the UGV. We then derive global information by scan matching local images to existing reference imagery and then use a pose graph to fuse the measurements to provide uninterrupted global positioning after loss of GPS signal. We show that our system generates visually accurate orthographic images of the environment, provides reliable global measurements, and maintains an accurate global state estimate in GPS-denied conditions.

Accepted to ICRA 2022

Conference Paper: IEEE Xplore Link

PUDLE: Prospecting Underground Liquid Extractor

Research Goal: Design a robotic system to collect water from subsurface ice deposits on Mars for the 2019 NASA Mars Ice Challenge.

Technical Report: Link

Program Website: Mars Ice Challenge

PAWES: Planetary Articulating Water Extractor

Research Goal: Design a robotic system to collect water from subsurface ice deposits on Mars for the 2018 NASA Mars Ice Challenge.

Accepted to 2019 IEEE Aerospace Conference

Conference Paper: Link

Program Website: Mars Ice Challenge