a b s t r a c t
This paper presents a new parameterization approach for the graph-based SLAM problem and reveals the
differences of two popular over-parameterized ways in the optimization procedure. In the SALM problem,
constraints or relative transformations between any two poses are generally separated into translations
plus 3D rotations, which are then described in a homogeneous transformation matrix (HTM) to simplify
computational operations. This however introduces added complexities in frequent conversions between
the HTM and state variables, due to their different representations. This new approach, unit dual
quaternion (UDQ), describes a spatial transformation as a screw with only 8 elements. We show that state
variables can be directly represented by UDQs, and how their relative transformations can be written with
the UDQ product, without the trivial computations of HTM. Then, we explore the performances of the
unit quaternion and the axis–angle representations in the graph-based SLAM problem, which have been
successfully applied to over parameterize perturbations under the assumption of small errors. Based on
public synthetic and real-world datasets in 2D and 3D environments, experimental results show that the
proposed approach reduces greatly the computational complexity while obtaining the same optimization
accuracies as the HTM-based algorithm, and the axis–angle representation is superior to be the quaternion
in the case of poor initial estimations.