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To autonomously move and operate objects in cluttered indoor environments, a service robot requires the ability of 3D scene perception. Though 3D object detection can provide an object-level environmental description to fill this gap, a robot always encounters incomplete object observation, recurring detections of the same object, error in detection, or intersection between objects when conducting detection continuously in a cluttered room. To solve these problems, we propose a two-stage 3D object detection algorithm which is to fuse multiple views of 3D object point clouds in the first stage and to eliminate unreasonable and intersection detections in the second stage. For each view, the robot performs a 2D object semantic segmentation and obtains 3D object point clouds. Then, an unsupervised segmentation method called Locally Convex Connected Patches (LCCP) is utilized to segment the object accurately from the background. Subsequently, the Manhattan Frame estimation is implemented to calculate the main orientation of the object and subsequently, the 3D object bounding box can be obtained. To deal with the detected objects in multiple views, we construct an object database and propose an object fusion criterion to maintain it automatically. Thus, the same object observed in multi-view is fused together and a more accurate bounding box can be calculated. Finally, we propose an object filtering approach based on prior knowledge to remove incorrect and intersecting objects in the object dataset. Experiments are carried out on both SceneNN dataset and a real indoor environment to verify the stability and accuracy of 3D semantic segmentation and bounding box detection of the object with multi-view fusion.

Significant grain refinement in cast metals can be achieved through the application of electric currents during the solidification process. The present paper investigates the distribution of electric currents on the grain size of solidified Al-7wt.%Si alloy under the application of electric current with constant parameters flowing through two parallel electrodes into the melt within a cylindrical mould. The distribution of electric current was controlled by applying an electrical insulation material coating, boron nitride (NB), to the sidewall of the electrodes. Experimental results showed that the employment of these insulated electrodes can reduce grain size in comparison with the reference case of electrodes without BN coating. Flow measurements were performed in Ga-20wt.%In-12wt.%Sn liquid metal. Higher intensity forced flow occurred when the sidewall of the electrodes was insulated. In order to understand the underlying mechanism behind the stronger forced flow, corresponding numerical simulations were performed to reveal the distributions of the electric current, magnetic field, Lorentz force, and the resultant forced flow. The results achieved indicate that the mechanism of grain refinement driven by electric current is dendrite fragmentation induced by forced flow. In addition, a novel approach to enhance the grain refinement without additional input of current energy was developed.