GTSAM is a library of C++ classes that implement smoothing and mapping (SAM) in robotics and vision, using factor graphs and Bayes networks as the underlying computing paradigm rather than sparse matrices.
Assuming I have a pose graph optimization problem of SE(3) poses. I would like to optimize only scale of translation(i.e. norm of translation vector), while keeping rotation and direction of translation fixed. Error would be in tangent space of SE(3).
How would one create such optimization in GTSAM? Do appropriate factors already exist or is only solution creating custom factors?
Hello @kkoledic1212 , did you find out the answer on your own?
I have recently tried to solve a similar problem, and I think there is no appropriate factor for translation scale in GTSAM.
Assuming I have a pose graph optimization problem of SE(3) poses. I would like to optimize only scale of translation(i.e. norm of translation vector), while keeping rotation and direction of translation fixed. Error would be in tangent space of SE(3).
How would one create such optimization in GTSAM? Do appropriate factors already exist or is only solution creating custom factors?