Sensors, Vol. 19, Pages 4092: Multi-View Fusion-Based 3D Object Detection for Robot Indoor Scene Perception

Sensors, Vol. 19, Pages 4092: Multi-View Fusion-Based 3D Object Detection for Robot Indoor Scene Perception Sensors doi: 10.3390/s19194092 Authors: Li Wang Ruifeng Li Jingwen Sun Xingxing Liu Lijun Zhao Hock Soon Seah Chee Kwang Quah Budianto Tandianus 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 cri...
Source: Sensors - Category: Biotechnology Authors: Tags: Article Source Type: research