Abstract :
[en] Factor graph optimization serves as a fundamental framework for robotic perception, enabling applications such as pose estimation, simultaneous localization and mapping (SLAM), structure-from-motion (SfM), and situational modeling. Traditionally, these methods solve unconstrained least squares problems using algorithms such as Gauss-Newton and Levenberg-Marquardt. However, extending factor graphs with native support for hard equality constraints can yield more accurate state estimates and broaden their applicability, particularly in planning and control. Prior work has addressed equality handling either by soft penalties (large weights) or by nested-loop Augmented Lagrangian (AL) schemes. In this paper, we propose a novel extension of factor graphs that seamlessly incorporates hard equality constraints without requiring additional optimization techniques. Our approach maintains the efficiency and flexibility of existing second-order optimization techniques while ensuring constraint satisfaction. To validate the proposed method, an autonomous-vehicle velocity-tracking optimal control problem is solved and benchmarked against an AL baseline, both implemented in g2o. Additional comparisons are conducted in GTSAM, where the penalty method and AL are evaluated against our g2o implementations. Moreover, we introduce
ecg2o
, a header-only C++ library that extends the widely used g2o library with full support for hard equality-constrained optimization. This library, along with demonstrative examples and the optimal control problem, is available as open source at
<jats:ext-link>https://github.com/snt-arg/ecg2o</jats:ext-link>
.
