Convex Hull-based Algebraic Constraint for Visual Quadric SLAM
This work addresses incremental improvements in visual SLAM for robotics and autonomous systems by enhancing object mapping accuracy.
The paper tackles the problem of imprecise constraints in quadric-based SLAM by introducing a convex hull-based algebraic constraint that leverages semantic segmentation to improve object reconstruction and localization, achieving better performance than existing methods on public datasets.
Using Quadrics as the object representation has the benefits of both generality and closed-form projection derivation between image and world spaces. Although numerous constraints have been proposed for dual quadric reconstruction, we found that many of them are imprecise and provide minimal improvements to localization.After scrutinizing the existing constraints, we introduce a concise yet more precise convex hull-based algebraic constraint for object landmarks, which is applied to object reconstruction, frontend pose estimation, and backend bundle adjustment.This constraint is designed to fully leverage precise semantic segmentation, effectively mitigating mismatches between complex-shaped object contours and dual quadrics.Experiments on public datasets demonstrate that our approach is applicable to both monocular and RGB-D SLAM and achieves improved object mapping and localization than existing quadric SLAM methods. The implementation of our method is available at https://github.com/tiev-tongji/convexhull-based-algebraic-constraint.