@article{hessianKorotkine2024,author={Korotkine, Vassili and Cohen, Mitchell and Forbes, James Richard},journal={IEEE Robotics and Automation Letters},title={A Hessian for Gaussian Mixture Likelihoods in Nonlinear Least Squares},year={2024},volume={9},number={9},pages={7891-7898},keywords={Optimization;Simultaneous localization and mapping;Optimization methods;State estimation;Standards;Newton method;Jacobian matrices;Localization;optimization and optimal control;probabilistic inference;sensor fusion;SLAM},doi={10.1109/LRA.2024.3432350},selected=true,code=https://github.com/decargroup/hessian_sum_mixtures,bibtex_show=true}
2023
IROS
navlie: A Python Package for State Estimation on Lie Groups
Charles Champagne Cossette, Mitchell Cohen, Vassili Korotkine, and 3 more authors
In 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2023
@inproceedings{10342362,author={Cossette, Charles Champagne and Cohen, Mitchell and Korotkine, Vassili and Del Castillo Bernal, Arturo and Shalaby, Mohammed Ayman and Forbes, James Richard},booktitle={2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},title={navlie: A Python Package for State Estimation on Lie Groups},year={2023},volume={},number={},pages={5282-5287},keywords={Manifolds;Estimation error;Navigation;Prototypes;Libraries;State estimation;Task analysis;Localization;Sensor Fusion;Software Tools for Robot Programming},doi={10.1109/IROS55552.2023.10342362},selected=true,code=https://github.com/decargroup/navlie/tree/main/navlie/utils,bibtex_show=true}
2022
RA-L
Koopman Linearization for Data-Driven Batch State Estimation of Control-Affine Systems
Zi Cong Guo, Vassili Korotkine, James R. Forbes, and 1 more author
@article{9645374,author={Guo, Zi Cong and Korotkine, Vassili and Forbes, James R. and Barfoot, Timothy D.},journal={IEEE Robotics and Automation Letters},title={Koopman Linearization for Data-Driven Batch State Estimation of Control-Affine Systems},year={2022},volume={7},number={2},pages={866-873},keywords={Kernel;State estimation;Control systems;Robots;Training;Nonlinear systems;Training data;Localization;probabilistic inference},doi={10.1109/LRA.2021.3133587},selected=true,bibtex_show=true}