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Research and Application of Semantic Point Cloud on Indoor Robots
Conference proceeding

Research and Application of Semantic Point Cloud on Indoor Robots

Shu Wen Dang, Cheng Yi Zhang and Yong Chen
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Conference Proceedings
International Conference on Communication and Information Systems (ICCIS), 5 (Chongqing, China, 10/15/2021–10/17/2021)
01/01/2021
DOI: 10.1109/ICCIS53528.2021.9645979

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Abstract

Conference Title: 2021 5th International Conference on Communication and Information Systems (ICCIS) Conference Start Date: 2021, Oct. 15 Conference End Date: 2021, Oct. 17 Conference Location: Chongqing, ChinaThe obstacle avoidance method based on visual and ultrasonic sensors and corresponding algorithms are widely researched and applied among route planning of indoor robot. This paper proposes a semantic point cloud construction method based on FC-DenseNets to analyze and compare the filtering performance of different parameters selection which being involved in the Statistical Outlier Removal filter. The original point cloud data is obtained by the RGB-D camera, and the best parameters setting as K=20 and α=1.0 is determined as conclusion. To achieve better image segmentation, FC-DenseNets and deep learning method is combined to contribute the point cloud with semantic information, which providing more richer environmental information for the robot’s mobile obstacle avoidance. Experimental results show that the proposed algorithm outperforms on simplified structure and distinct segmentation of environmental information.
Algorithms Machine Learning Semantics Image segmentation Information systems Obstacle avoidance Outliers (statistics) Parameters Robots Route planning

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