Efficient Object-Oriented Semantic Mapping With Object Detector

Service robots gain both geometric and semantic information about the environment with the help of semantic mapping, providing more intelligent services. However, a majority of studies for semantic mapping thus far require priori knowledge 3D object models or maps with a few object categories that neglect separate individual objects. In view of these problems, an object-oriented 3D semantic mapping method is proposed by combining state-of-the-art deep-learning-based instance segmentation and a visual simultaneous localization and mapping (SLAM) algorithm, which helps robots not only gain navigation-oriented geometric information about the surrounding environment, but also obtain individually-oriented attribute and location information about the objects. Meanwhile, an object recognition and target association algorithm applied to continuous image frames is proposed by combining visual SLAM, which uses visual consistency between image frames to promote the result of object matching and recognition over continuous image frames, and improve the object recognition accuracy. Finally, a 3D semantic mapping system is implemented based on Mask R-CNN and ORB-SLAM2 frameworks. A simulation experiment is carried out on the ICL-NUIM dataset and the experimental results show that the system can generally recognize all the types of objects in the scene and generate fine point cloud models of these objects, which verifies the effectiveness of our algorithm.

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Online Object-Oriented Semantic Mapping in Triger Classification Environment

10.1007/978-3-030-89134-3_28 ◽

2021 ◽

pp. 304-313

Author(s):

Wanlei Li ◽

Yujing Chen ◽

Haixiang Zhou ◽

Minghui Hua ◽

Yunjiang Lou

Keyword(s):

Object Oriented ◽

Semantic Mapping

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Meaningful maps with object-oriented semantic mapping

2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) ◽

10.1109/iros.2017.8206392 ◽

2017 ◽

Cited By ~ 51

Author(s):

Niko Sunderhauf ◽

Trung T. Pham ◽

Yasir Latif ◽

Michael Milford ◽

Ian Reid

Keyword(s):

Object Oriented ◽

Semantic Mapping

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Congratulations ! JACIII Best Paper and Young Researcher Awards 2021

Journal of Advanced Computational Intelligence and Intelligent Informatics ◽

10.20965/jaciii.2021.p0925 ◽

2021 ◽

Vol 25 (6) ◽

pp. 925-928

Author(s):

Keyword(s):

Review Paper ◽

Learning Algorithm ◽

Object Oriented ◽

Young Researcher ◽

Semantic Mapping ◽

Paper Award ◽

Editorial Boards ◽

Award Winning ◽

Deep Forest ◽

Network Object

We are pleased to announce that the JACIII Awards of 2021 have been decided by the JACIII editorial boards. This year, the award winning papers were severely and fairly selected among 362 papers published in JACIII Vols. 22 (2018) to 24 (2020) and there was no entries that deserved the Best Review Paper award. The award ceremony was held online in order to prevent spreading of COVID-19. JACIII BEST PAPER AWARD 2021 Sotetsu Suzugamine, Takeru Aoki, Keiki Takadama, and Hiroyuki Sato Self-Structured Cortical Learning Algorithm by Dynamically Adjusting Columns and Cells JACIII Vol.24 No.2, pp. 185-198, 2020. JACIII YOUNG RESEARCHER AWARD 2021 JACIII YOUNG RESEARCHER AWARD 2021 Xiaobo Liu Jinxin Chi Emotion Recognition Based on Multi-Composition Deep Forest and Transferred Convolutional Neural Network Object-Oriented 3D Semantic Mapping Based on Instance Segmentation By Xiaobo Liu, Xu Yin, Min Wang, Yaoming Cai, and Guang Qi By Jinxin Chi, Hao Wu, and Guohui Tian JACIII Vol.23 No.5, pp. 883-890, 2019. JACIII Vol.23 No.4, pp. 695-704, 2019.

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