Cited By
View all- Wang CZhang XZang XLiu YDing GYin WZhao J(2020)Feature Sensing and Robotic Grasping of Objects with Uncertain Information: A ReviewSensors10.3390/s2013370720:13(3707)Online publication date: 2-Jul-2020
Real-Time Object Recognition from Streaming LiDAR Point Cloud Data
Authors: 
Sambasiva Rao Gangineni, Harshad Reddy Nalla, 
Saeed Fathollahzadeh, 
Kia TeymourianAuthors Info & Claims
Sambasiva Rao Gangineni, Harshad Reddy Nalla, Saeed Fathollahzadeh, Kia TeymourianAuthors Info & ClaimsPages 214 - 219
Abstract
In many robotic applications, LiDAR (Light Detection and Ranging) scanner is used to gather data about the environment. Applications like autonomous vehicles require real-time processing of LiDAR point cloud data with high accuracy.
We describe in this paper, our implementation for DEBS 2019 Grand Challenge for an object recognition system from high-speed LiDAR data stream. Our system includes a data processing pipeline with 3 main stages, 1. LiDAR data filtering 2. Object segmentation and noise reduction 3. Multi-class object classification using Convolutional Neural Network (CNN). Our evaluation shows that we can classify objects with high accuracy using the point cloud data and neural network. However, we observed that the classification may fail if the object segmentation is not separating objects correctly in different segments especially when the objects are largely covering each other. We proposed a pre-processing approach for object segmentation based on separating LiDAR data into multiple area sectors before segmenting the objects.
References
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Oleh Bodunov, Vincenzo Gulisano, Zbigniew Jerzak, Andre Martin, Hannaneh Najdataei, Pavel Smirnov, Martin Strohbach, and Holger Ziekow. 2019. The DEBS 2019 grand challenge. In Proceedings of the 11th ACM International Conference on Distributed and Event-based Systems, DEBS '19, Darmstadt, Germany, June 24 - 28, 2019.
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Andreas Geiger, Philip Lenz, and Raquel Urtasun. 2012. Are we ready for autonomous driving? the kitti vision benchmark suite. In 2012 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 3354--3361.
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Jing Huang and Suya You. 2016. Point cloud labeling using 3D Convolutional Neural Network. In 23rd International Conference on Pattern Recognition, ICPR 2016, Cancún, Mexico, December 4-8, 2016. IEEE, 2670--2675.
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Daniel Maturana and Sebastian Scherer. 2015. VoxNet: A 3D Convolutional Neural Network for real-time object recognition. In IROS. IEEE, 922--928.
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Bichen Wu, Alvin Wan, Xiangyu Yue, and Kurt Keutzer. 2018. SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud. In 2018 IEEE International Conference on Robotics and Automation, ICRA 2018, Brisbane, Australia, May 21-25, 2018. IEEE, 1887--1893.
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Index Terms
- Real-Time Object Recognition from Streaming LiDAR Point Cloud Data
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Published In
June 2019
291 pages
ISBN:9781450367943
DOI:10.1145/3328905
Copyright © 2019 ACM.
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Publication History
Published: 24 June 2019
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DEBS '19: The 13th ACM International Conference on Distributed and Event-based Systems
June 24 - 28, 2019
Darmstadt, Germany
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DEBS '19 Paper Acceptance Rate 13 of 47 submissions, 28%;
Overall Acceptance Rate 145 of 583 submissions, 25%
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View all- Wang CZhang XZang XLiu YDing GYin WZhao J(2020)Feature Sensing and Robotic Grasping of Objects with Uncertain Information: A ReviewSensors10.3390/s2013370720:13(3707)Online publication date: 2-Jul-2020
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