@article {Li2022,
	title = {3-D Inter-Robot Relative Localization via Semidefinite Optimization},
	journal = {IEEE Robotics and Automation Letters},
	volume = {7},
	year = {2022},
	pages = {10081{\textendash}10088},
	publisher = {IEEE},
	abstract = {In this letter, the 3-D inter-robot relative localization problem is addressed using noise-corrupted odometric and distance measurements. Unlike the existing solutions, we are devoted to providing a relative localization method that has an {\textquotedblleft}overall best performance,{\textquotedblright} which means that the tradeoffs between the estimation accuracy (EA), the number of measurements (NoMs), and the computation efficiency (CE) are considered. We demonstrate that an existing formulation of the 3-D relative localization problem, the square distances weighted least square (SD-WLS), can be equivalently reformulated as a non-convex quadratic constrained quadratic programming (QCQP) problem. Further, to handle the non-convex nature of the QCQP problem, we adopt the semidefinite programming (SDP) relaxation approach, which drops the rank constraint and recovers the solution of the QCQP via an eigenvalue decomposition strategy. Finally, a refinement step is introduced to solve the problem that the quadratic constraints might not be satisfied due to the SDP relaxation. The simulation and experiment results show that, compared to existing methods, our method has the best overall performance when the three factors, i.e., EA, NoMs, and CE, are important for a relative localization application.},
	keywords = {3-D inter-robot relative localization, Distance measurement, Global Positioning System, Location awareness, non-convex optimization, QCQP, Quaternions, Robots, SDP, sensors, Sun},
	issn = {2377-3774},
	doi = {10.1109/LRA.2022.3192888},
	author = {Ming Li and Tin Lun Lam and Zhiyong Sun}
}
@conference {Tu2021,
	title = {Graph Convolutional Network based Configuration Detection for Freeform Modular Robot Using Magnetic Sensor Array},
	booktitle = {2021 IEEE International Conference on Robotics and Automation (ICRA)},
	year = {2021},
	pages = {4252{\textendash}4258},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Xi{\textquoteright}an, China},
	abstract = {Modular self-reconfigurable robotic (MSRR) systems are potentially more robust and more adaptive than conventional systems. Following our previous work where we proposed a freeform MSRR module called FreeBOT, this paper presents a novel configuration detection system for FreeBOT using a magnetic sensor array. A FreeBOT module can be connected by up to 11 modules, and the proposed configuration detection system can locate a variable number of connection points accurately in real-time. By equipping FreeBOT with 24 magnetic sensors, the magnetic field density produced by magnets and steel spherical shells can be monitored. The connectable area is split into 199 non-uniform regions, including 84 uniform regions. Using a Graph Convolutional Network (GCN) based algorithm, the connection points can be located accurately under ferromagnetic environments. The system can locate a variable number of connection points for such a region division with only single connection point training data. Finally, the localization algorithm can run faster than 40 Hz on FreeBOT. With the real-time configuration detection system, the FreeBOT system has the potential to reconfigure automatically and accurately.},
	keywords = {Conferences, Location awareness, Magnetic sensors, Real-time systems, Robot sensing systems, Steel, Training data},
	isbn = {978-1-7281-9078-5},
	issn = {1050-4729},
	doi = {10.1109/ICRA48506.2021.9561340},
	author = {Yuxiao Tu and Guanqi Liang and Tin Lun Lam}
}
@article {Guo2021,
	title = {Semantic Histogram Based Graph Matching for Real-Time Multi-Robot Global Localization in Large Scale Environment},
	journal = {IEEE Robotics and Automation Letters},
	volume = {6},
	year = {2021},
	pages = {8349{\textendash}8356},
	publisher = {IEEE},
	abstract = {The core problem of visual multi-robot simultaneous localization and mapping (MR-SLAM) is how to efficiently and accurately perform multi-robot global localization (MR-GL). The difficulties are two-fold. The first is the difficulty of global localization for significant viewpoint difference. Appearance-based localization methods tend to fail under large viewpoint changes. Recently, semantic graphs have been utilized to overcome the viewpoint variation problem. However, the methods are highly time-consuming, especially in large-scale environments. This leads to the second difficulty, which is how to perform real-time global localization. In this letter, we propose a semantic histogram based graph matching method that is robust to viewpoint variation and can achieve real-time global localization. Based on that, we develop a system that can accurately and efficiently perform MR-GL for both homogeneous and heterogeneous robots. The experimental results show that our approach is about 30 times faster than Random Walk based semantic descriptors. Moreover, it achieves an accuracy of 95\% for global localization, while the accuracy of the state-of-the-art method is 85\%.},
	keywords = {Global localization, Histograms, large viewpoint difference, Location awareness, multi-robot map merging, Real-time systems, semantic SLAM, Semantics, Simultaneous localization and mapping, Three-dimensional displays, Visualization},
	issn = {2377-3774},
	doi = {10.1109/LRA.2021.3058935},
	author = {Xiyue Guo and Junjie Hu and Junfeng Chen and Fuqin Deng and Tin Lun Lam}
}
