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End-to-End Synthetic LiDAR Point Cloud Data Generation and Deep Learning Validation
Technical Paper
2022-01-0164
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
LiDAR sensors are common in automated driving due to their high accuracy. However, LiDAR processing algorithm development suffers from lack of diverse training data, partly due to sensors’ high cost and rapid development cycles. Public datasets (e.g. KITTI) offer poor coverage of edge cases, whereas these samples are essential for safer self-driving. We address the unmet need for abundant, high-quality LiDAR data with the development of a synthetic LiDAR point cloud generation tool and validate this tool’s performance using the KITTI-trained PIXOR object detection model. The tool uses a single camera raycasting process and filtering techniques to generate segmented and annotated class specific datasets. This approach will support low-cost bulk generation of accurate data for training advanced selfdriving algorithms, with configurability to simulate existing and upcoming LiDAR configurations possessing varied channels, range, vertical and horizontal fields of view, and angular resolution. In comparison with virtual LiDAR solutions like CARLA [1], this tool requires no game development knowledge and is faster to set up: sensor customization can be done in a front-end panel enabling users to focus more on data generation. The simulator is developed using the Unity Game Engine in conjunction with free and open-source assets, and a build will be shared with the AV community before an open-source release.
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Citation
Karur, K., Pappas, G., Siegel, J., and Zhang, M., "End-to-End Synthetic LiDAR Point Cloud Data Generation and Deep Learning Validation," SAE Technical Paper 2022-01-0164, 2022, https://doi.org/10.4271/2022-01-0164.Also In
References
- Deschaud , J.-E. 2021
- Barnett , J. , Gizinski , N. , Mondragon-Parra , E. , Siegel , J.E. et al. Automated Vehicles Sharing the Road: Surveying Detection and Localization of Pedalcyclists IEEE Transactions on Intelligent Vehicles 2020 1 1
- Karur , K. , Sharma , N. , Dharmatti , C. , and Siegel , J.E. A Survey of Path Planning Algorithms for Mobile Robots 2021
- Engelcke , M. , Rao , D. , Wang , D. , Tong , C. et al. Vote3deep: Fast Object Detection in 3d Point Clouds using Efficient Convolutional Neural Networks 09 2016
- Girshick , R. , Donahue , J. , Darrell , T. , and Malik , J. Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation 2014 IEEE Conference on Computer Vision and Pattern Recognition 2014 580 587
- Girshick , R.B. Fast R-Cnn 2015 IEEE International Conference on Computer Vision (ICCV) 1440 1448 2015
- Ren , S. , He , K. , Girshick , R. , and Sun , J. Faster R-Cnn: Towards Real-Time Object Detection with Region Proposal Networks IEEE Transactions on Pattern Analysis and Machine Intelligence 39 2015 06
- Dai , J. , Li , Y. , He , K. , and Sun , J. R-Fcn: Object Detection Via Region-based Fully Convolutional Networks Proceedings of the 30th International Conference on Neural Information Processing Systems, Ser. NIPS’16 , Red Hook, NY, USA 2016 379 387
- Redmon , J. , Divvala , S. , Girshick , R. , and Farhadi , A. You Only Look Once: Unified, Real-Time Object Detection 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2016 779 788
- Liu , W. , Anguelov , D. , Erhan , D. , Szegedy , C. et al. Ssd: Single Shot Multibox Detector ECCV 2016
- Krizhevsky , A. , Sutskever , I. , and Hinton , G. Imagenet Classification with Deep Convolutional Neural Networks Neural Information Processing Systems 25 2012 01
- Deng , J. , Dong , W. , Socher , R. , Li , L.-J. et al. Imagenet: A Large-Scale Hierarchical Image Database 2009 IEEE Conference on Computer Vision and Pattern Recognition 2009 248 255
- He , K. , Zhang , X. , Ren , S. , and Sun , J. Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition IEEE Transactions on Pattern Analysis and Machine Intelligence 37 9 2015 1904 1916
- Uijlings , J. , Sande , K. , Gevers , T. , and Smeulders , A. Selective Search for Object Recognition International Journal of Computer Vision 104 09 2013 154 171
- Pont-Tuset , J. , Arbeláez , P. , Barron , J.T. , Marqués , F. et al. Multiscale Combinatorial Grouping for Image Segmentation and Object Proposal Generation IEEE Transactions on Pattern Analysis and Machine Intelligence 39 2017 128 140
- Everingham , M. , Gool , L. , Williams , C.K. , Winn , J. et al. The Pascal Visual Object Classes (VOC) Challenge Int. J. Comput. Vision 88 2 Jun. 2010 303 338 [Online]. Available: https://doi.org/10.1007/s11263-009-0275-4
- Russakovsky , O. , Deng , J. , Su , H. , Krause , J. et al. Imagenet Large Scale Visual Recognition Challenge International Journal of Computer Vision 115 2014 09
- Huang , J. , Rathod , V. , Sun , C. , Zhu , M. et al. Speed/Accuracy Trade-Offs for Modern Convolutional Object Detectors 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Los Alamitos, CA, USA: IEEE Computer Society Jul 2017 3296 3297 https://doi.ieeecomputersociety
- https://www.neonscience.org/resources/learning-hub/tutorials/lidar-basics
- https://info.vercator.com/blog/lidar-vs-point-clouds
- Siegel , J. and Krishnan , S. Cultivating Invisible Impact with Deep Technology and Creative Destruction Journal of Innovation Management 8 3 2020 6 19
- Geiger , A. , Lenz , P. , Stiller , C. , and Urtasun , R. Vision Meets Robotics: The KITTI Dataset The International Journal of Robotics Research 32 11 2013 1231 1237 [Online]. Available: https://doi.org/10.1177/0278364913491297
- Pappas , G. , Siegel , J.E. , Politopoulos , K. , and Sun , Y. A Gamified Simulator and Physical Platform for Self-Driving Algorithm Training and Validation Electronics 10 9 2021
- Buyuksalih , I. , Bayburt , S. , Buyuksalih , G. , Baskaraca , A.P. et al. 3D Modelling and Visualization Based on the Unity Game Engine - Advantages and Challenges 4 4W4 2017 161 166
- Juliani , A. , Berges , V.-P. , Teng , E. , Cohen , A. et al. 2018 1 28 http://arxiv.org/abs/1809.02627
- Unity Technologies Windridge City 2019 https://assetstore.unity.com/packages/3d/environments/roadways/windridge-city-132222
- Velodyne HDL-64E: High Definition Real-Time 3D LiDAR 2018 https://www.goetting-agv.com/dateien/downloads/63-9194{_}Rev-G{_}HDL-64E{_}S3{_}SpecSheet{_}Web.pdf
- Dosovitskiy , A. , Ros , G. , Codevilla , F. , López , A. et al. arXiv 1 16 2017
- Dworak , D. , Ciepiela , F. , Derbisz , J. , Izzat , I. et al. Performance of LiDAR Object Detection Deep Learning Architectures based on Artificially Generated Point Cloud Data from CARLA Simulator 2019 24th International Conference on Methods and Models in Automation and Robotics (MMAR) 2019 600 605
- Yang , B. , Luo , W. , and Urtasun , R. Pixor: Real-Time 3d Object Detection from Point Clouds 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition 2018 7652 7660
- Zhou , Y. and Tuzel , O. 2018 4490 4499
- He , K. , Gkioxari , G. , Dollár , P. , and Girshick , R. Mask R-Cnn IEEE Transactions on Pattern Analysis and Machine Intelligence 42 2 2020 386 397
- Huang , L. , Yang , Y. , Deng , Y. , and Yu , Y. ArXiv 2015
- Zhou , X. , Yao , C. , Wen , H. , Wang , Y. et al. East: An efficient and accurate scene text detector 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017 2642 2651
- Shelhamer , E. , Long , J. , and Darrell , T. Fully Convolutional Networks for Semantic Segmentation IEEE Transactions on Pattern Analysis and Machine Intelligence 39 4 2017 640 651
- Lin , T.-Y. , Goyal , P. , Girshick , R.B. , He , K. et al. Focal Loss for Dense Object Detection 2017 IEEE International Conference on Computer Vision (ICCV) 2017 2999 3007