Object-based SLAM utilizing unambiguous pose parameters considering general symmetry types

Status: accepted for publication in IEEE International Conference on Robotics and Automation (ICRA) 2023
Category : Object-based SLAM, symmetry, pose ambiguity
Author : Taekbeom Lee*, Youngseok Jang*, H. Jin Kim (* Equal contribution)
Abstract: Existence of symmetric objects, whose observation at
different viewpoints can be identical, can deteriorate the
performance of simultaneous localization and mapping
(SLAM). This work proposes a system for robustly optimizing
the pose of cameras and objects even in the presence of
symmetric objects. We classify objects into three
categories depending on their symmetry characteristics,
which is efficient and effective in that it allows to deal
with general objects and the objects in the same category
can be associated with the same type of ambiguity. Then we
extract only the unambiguous parameters corresponding to
each category and use them in data association and joint
optimization of the camera and object pose. The proposed
approach provides significant robustness to the SLAM
performance by removing the ambiguous parameters and
utilizing as much useful geometric information as possible.
Comparison with baseline algorithms confirms the superior
performance of the proposed system in terms of object
tracking and pose estimation, even in challenging scenarios
where the baseline fails.
Contact : {ltb1128, duscjs59}@snu.ac.kr