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Robust Graph Optimization for Robotic Perception
Graph optimization is a key technique employed within Simultaneous Localization And Mapping (SLAM) frameworks,
enabling the automation of robot navigation. By encoding a mobile robot’s experiences of the world in a graph (i.e., the
robot poses and the sensor readings), such techniques offer a robust way of estimating the robot’s trajectory and the
map of its environment. However, these techniques are often computationally demanding and their performance is
severely hampered by outliers originating from erroneous sensor measurements or incorrect loop closure detections.
To address this challenge, robust graph optimization methods, such as Pairwise Consistency Maximisation (PCM) [2]
and Graduated Non-Convexity (GNC) [3], have been devised aiming to enhance the resilience of SLAM algorithms
against such outliers, however robustness and complexity both remain open challenges to date.
This project aims to build on the strengths of existing works on robust graph optimization in the context of SLAM, and
guide the development and implementation of optimization methods robust to outliers caused by erroneous
measurements and loop closure detections. Moreover, techniques such as incremental computation will be
investigated to better tailor graph optimization to real-time SLAM applications, which is a requirement in robot
navigation.
This project is offered by the Vision for Robotics Lab (www.v4rl.com) at ETH Zurich and the University of Cyprus.
Students undertaking the project may have the opportunity to visit the lab at the University of Cyprus, but this is not
required.
Keywords: SLAM; Optimization; Mobile Robots; Computer Vision
Graph optimization is a key technique employed within Simultaneous Localization And Mapping (SLAM) frameworks,
enabling the automation of robot navigation. By encoding a mobile robot’s experiences of the world in a graph (i.e., the
robot poses and the sensor readings), such techniques offer a robust way of estimating the robot’s trajectory and the
map of its environment. However, these techniques are often computationally demanding and their performance is
severely hampered by outliers originating from erroneous sensor measurements or incorrect loop closure detections.
To address this challenge, robust graph optimization methods, such as Pairwise Consistency Maximisation (PCM) [2]
and Graduated Non-Convexity (GNC) [3], have been devised aiming to enhance the resilience of SLAM algorithms
against such outliers, however robustness and complexity both remain open challenges to date.
This project aims to build on the strengths of existing works on robust graph optimization in the context of SLAM, and
guide the development and implementation of optimization methods robust to outliers caused by erroneous
measurements and loop closure detections. Moreover, techniques such as incremental computation will be
investigated to better tailor graph optimization to real-time SLAM applications, which is a requirement in robot
navigation.
This project is offered by the Vision for Robotics Lab (www.v4rl.com) at ETH Zurich and the University of Cyprus.
Students undertaking the project may have the opportunity to visit the lab at the University of Cyprus, but this is not
required.
references
[1] Fischler, Martin A., and Robert C. Bolles. "Random sample consensus: a paradigm for model fitting with applications to image
analysis and automated cartography." Communications of the ACM 24.6 (1981): 381-395.
[2] J. G. Mangelson, et. al, "Pairwise Consistent Measurement Set Maximization for Robust Multi-Robot Map Merging," 2018 IEEE
International Conference on Robotics and Automation (ICRA).
[3] Yang, Heng, et al. "Graduated non-convexity for robust spatial perception: From non-minimal solvers to global outlier rejection." IEEE
Robotics and Automation Letters 5.2 (2020): 1127-1134.
Graph optimization is a key technique employed within Simultaneous Localization And Mapping (SLAM) frameworks, enabling the automation of robot navigation. By encoding a mobile robot’s experiences of the world in a graph (i.e., the robot poses and the sensor readings), such techniques offer a robust way of estimating the robot’s trajectory and the map of its environment. However, these techniques are often computationally demanding and their performance is severely hampered by outliers originating from erroneous sensor measurements or incorrect loop closure detections. To address this challenge, robust graph optimization methods, such as Pairwise Consistency Maximisation (PCM) [2] and Graduated Non-Convexity (GNC) [3], have been devised aiming to enhance the resilience of SLAM algorithms against such outliers, however robustness and complexity both remain open challenges to date. This project aims to build on the strengths of existing works on robust graph optimization in the context of SLAM, and guide the development and implementation of optimization methods robust to outliers caused by erroneous measurements and loop closure detections. Moreover, techniques such as incremental computation will be investigated to better tailor graph optimization to real-time SLAM applications, which is a requirement in robot navigation. This project is offered by the Vision for Robotics Lab (www.v4rl.com) at ETH Zurich and the University of Cyprus. Students undertaking the project may have the opportunity to visit the lab at the University of Cyprus, but this is not required.
references [1] Fischler, Martin A., and Robert C. Bolles. "Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography." Communications of the ACM 24.6 (1981): 381-395. [2] J. G. Mangelson, et. al, "Pairwise Consistent Measurement Set Maximization for Robust Multi-Robot Map Merging," 2018 IEEE International Conference on Robotics and Automation (ICRA). [3] Yang, Heng, et al. "Graduated non-convexity for robust spatial perception: From non-minimal solvers to global outlier rejection." IEEE Robotics and Automation Letters 5.2 (2020): 1127-1134.
The work packages of this project involve a literature review and experiments on existing research on robust graph
optimization methods, exploring approaches to address known pitfalls of existing works, and validating the devised
methodology on real data.
The work packages of this project involve a literature review and experiments on existing research on robust graph optimization methods, exploring approaches to address known pitfalls of existing works, and validating the devised methodology on real data.
The students need to have programming experience with Python or C++.
The students need to have programming experience with Python or C++.
Please send transcripts and CV to liu.xiangyu@ucy.ac.cy and pilucas@ethz.ch
Please send transcripts and CV to liu.xiangyu@ucy.ac.cy and pilucas@ethz.ch
ETH_UCY Project - Robust Graph Optimization.pdf
