RT Journal Article
T1 Initialization of 3D Pose Graph Optimization using Lagrangian duality
A1 Briales Garcia, Jesus
A1 González-Jiménez, Antonio Javier
K1 Programación (Matemáticas)
AB Pose Graph Optimization (PGO) is the de factochoice to solve the trajectory of an agent in Simultaneous Localization and Mapping (SLAM). The Maximum Likelihood Estimation (MLE) for PGO is a non-convex problem for which no known technique is able to guarantee a globally optimal solution under general conditions. In recent years, Lagrangian duality has proved suitable to provide good, frequently tight relaxations of the hard PGO problem through convex Semidefinite Programming (SDP). In this work, we build from the state-of-the-art Lagrangian relaxation [1] and contribute a complete recovery procedure that, given the (tractable) optimal solutionof the relaxation, provides either the optimal MLE solution if the relaxation is tight, or a remarkably good feasible guess if the relaxation is non-tight, which occurs in specially challenging PGO problems (very noisy observations, low graph connectivity, etc.). In the latter case, when used for initialization of local iterative methods, our approach outperforms other state-ofthe-art approaches converging to better solutions. We support our claims with extensive experiments.
PB IEEE
YR 2017
FD 2017-05
LK http://hdl.handle.net/10630/14454
UL http://hdl.handle.net/10630/14454
LA eng
NO Int. Conf. on Robotics and Automation (ICRA).
NO University of Malaga travel grant, theSpanish grant program FPU14/06098 and the project PROMOVE (DPI2014-55826-R), funded by the Spanish Government and the "European Regional Development Fund". Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 25 ene 2026