Quantitative Comparison of LiDAR Point Cloud Segmentation for Autonomous Vehicles-Reference-Cited by-同舟云学术

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Quantitative Comparison of LiDAR Point Cloud Segmentation for Autonomous Vehicles

Published:2021-09 Issue: Volume: Page:
ISSN:
Container-title:2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall)
language:
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Author:

Anand Bhaskar,Barsaiyan Vivek,Senapati Mrinal,Rajalakshmi P.

Funder

Ministry of Electronics and Information Technology (MeitY)

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9624467/9625043/09625507.pdf?arnumber=9625507

Cited by 6 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A Hierarchical LiDAR Simulation Framework Incorporating Physical Attenuation Response in Autonomous Driving Scenarios;IEEE Transactions on Intelligent Transportation Systems;2024-06

2. Real-time 3D Map Generation and Analysis Using Multi-channel LiDAR Sensor;2024 IEEE 9th International Conference for Convergence in Technology (I2CT);2024-04-05

3. LiDAR-Based Optimized Normal Distribution Transform Localization on 3-D Map for Autonomous Navigation;IEEE Open Journal of Instrumentation and Measurement;2024

4. LiDAR and Camera Raw Data Sensor Fusion in Real-Time for Obstacle Detection;2023 IEEE Sensors Applications Symposium (SAS);2023-07-18

5. BEV Approach Based Efficient Object Detection using YoloV4 for LiDAR Point Cloud;2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring);2023-06

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