Multi-View Registration of Partially Overlapping Point Clouds for Robotic Manipulation

Point cloud registration is a fundamental task in intelligent robots, aiming to achieve globally consistent geometric structures and providing data support for robotic manipulation. Due to the limited view of measurement devices, it is necessary to collect point clouds from multiple views to construct a complete model. Previous multi-view registration methods rely on sufficient overlap and registering all pairs of point clouds, resulting in slow convergence and high cumulative errors. To solve these challenges, we present a multi-view registration method based on the point-to-plane model and pose graph. We introduce a robust kernel into the objective function to diminish registration errors caused by mismatched points. Additionally, an enhanced Euclidean clustering method is proposed for extracting object point clouds. Subsequently, by establishing pose constraints on non-adjacent frames of point clouds, the cumulative error is reduced, achieving global optimization based on the pose graph. Experimental results demonstrate the robustness of our method with respect to overlap ratios, successfully registering point clouds with overlap ratio exceeding 30$%. In comparison to other techniques, our method can reduce the E (R) of multi-view registration by 13.54$% and E (t) by 18.72$%, effectively reducing the cumulative error.