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Existence of symmetric objects, whose observation at different viewpoints canbe identical, can deteriorate the performance of simultaneous localization andmapping(SLAM). This work proposes a system for robustly optimizing the pose ofcameras and objects even in the presence of symmetric objects. We classifyobjects into three categories depending on their symmetry characteristics,which is efficient and effective in that it allows to deal with general objectsand the objects in the same category can be associated with the same type ofambiguity. Then we extract only the unambiguous parameters corresponding toeach category and use them in data association and joint optimization of thecamera and object pose. The proposed approach provides significant robustnessto the SLAM performance by removing the ambiguous parameters and utilizing asmuch useful geometric information as possible. Comparison with baselinealgorithms confirms the superior performance of the proposed system in terms ofobject tracking and pose estimation, even in challenging scenarios where thebaseline fails.

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