Detect Orientation of Symmetric Objects from Monocular Camera to Enhance Landmark Estimations in Object SLAM

Abstract

Object simultaneous localization and mapping (SLAM) introduces object-level landmarks to the map and helps robots to further perceive their surroundings. As one of the most preferred landmark representations, ellipsoid has a dense mathematical expression and can represent the occupied space of objects with high accuracy. However, the orientations of ellipsoid approximations often fail to coincide with the orientation of objects. To further improve the performance of object SLAM systems with ellipsoid landmarks, we innovatively propose a strategy that first extracts the orientations of those symmetric human-made objects in a single frame and then implements the results of the orientation as a back-end constraint factor of the ellipsoid landmarks. Experimental results obtained show that, compared with the baseline, the proposed orientation detection method can reduce the orientation error by more than 46.5% in most tested datasets and improves the accuracy of mapping. The average translation, rotation and shape error improved by 63.4%, 61.7% and 42.4%, respectively, compared with quadric-SLAM. With only 9 ms additional time cost of each frame, the object SLAM system integrated with our proposed method can still run in real time.