Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/142133
Title: Multi-view fusion-based 3D object detection for robot indoor scene perception
Authors: Wang, Li
Li, Ruifeng
Sun, Jingwen
Liu, Xingxing
Zhao, Lijun
Seah, Hock Soon
Quah, Chee Kwang
Tandianus, Budianto
Keywords: Engineering::Computer science and engineering
Issue Date: 2019
Source: Wang, L., Li, R., Sun, J., Liu, X., Zhao, L., Seah, H. S., . . . Tandianus, B. (2019). Multi-view fusion-based 3D object detection for robot indoor scene perception. Sensors, 19(19), 4092-. doi:10.3390/s19194092
Journal: Sensors
Abstract: 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.
URI: https://hdl.handle.net/10356/142133
ISSN: 1424-8220
DOI: 10.3390/s19194092
Rights: © 2019 The Authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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