|
|
激光雷达视窗表面灰尘污染模拟物研究
|
Abstract:
本研究深入探讨了视窗表面不同灰尘污染模拟对激光雷达性能的影响，以混合固态激光雷达为对象，通过多维度点云评价指标全面评估激光雷达在不同污染条件下的感知表现。预实验结果表明，污染于激光雷达激光发射器处影响最大，正式实验中研究了不同灰尘污染模拟物对激光雷达点云质量的影响，实验结果表明：污染对激光雷达测距性能无显著影响且不同颜色亚克力片和不同厚度橡胶片可以模拟灰尘污染。其中不透明亚克力片和橡胶片可以模拟重度灰尘污染，不同颜色的半透明亚克力片可以模拟轻度灰尘污染，通过改变亚克力片的颜色和透明度可以模拟不同程度的灰尘污染。该研究结果为进一步研究激光雷达视窗灰尘污染以及相关算法开发提供了基础。
This study comprehensively explores the impact of different contamination simulations on the performance of a mixed solid-state lidar, focusing on the window surface. Through multidimensional point cloud evaluation metrics, the lidar’s perception performance is thoroughly assessed under various contamination conditions. Preliminary experimental results indicate that contamination at the lidar’s laser emitter has the greatest impact. In the formal experiments, the influence of different contamination simulation materials on lidar point cloud quality is investigated. The results show that contamination has no significant impact on lidar ranging performance, and various colored acrylic sheets and rubber sheets can simulate contamination. Opaque acrylic sheets and rubber sheets can simulate heavy contamination, while differently colored translucent acrylic sheets can simulate mild contamination. By changing the color and transparency of the acrylic sheets, different degrees of contamination can be simulated. The findings of this study provide a foundation for further research on lidar window contamination and the development of related algorithms.
| [1] | Liu, J., Sun, Q., Fan, Z., et al. (2018) TOF Lidar Development in Autonomous Vehicle. 2018 IEEE 3rd Optoelectronics Global Conference (OGC), Shenzhen, 4-7 September 2018, 185-190. https://doi.org/10.1109/OGC.2018.8529992 |
| [2] | Zhang, J., Xiao, W., Coifman, B., et al. (2020) Vehicle Tracking and Speed Estimation from Roadside Lidar. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 5597-5608.
https://doi.org/10.1109/JSTARS.2020.3024921 |
| [3] | Tang, L., Shi, Y., He, Q., Sadek, A.W. and Qiao, C. (2020) Performance Test of Autonomous Vehicle Lidar Sensors under Different Weather Conditions. Transportation Research Record, 2674, 319-329.
https://doi.org/10.1177/0361198120901681 |
| [4] | Heinzler, R., Schindler, P., Seekircher, J., Ritter, W. and Stork, W. (2019) Weather Influence and Classification with Automotive Lidar Sensors. 2019 IEEE Intelligent Vehicles Symposium (IV), Paris, 9-12 June 2019, 1527-1534.
https://doi.org/10.1109/IVS.2019.8814205 |
| [5] | Filgueira, A., González-Jorge, H., Lagüela, S., et al. (2017) Quan-tifying the Influence of Rain in LiDAR Performance. Measurement, 95, 143-148. https://doi.org/10.1016/j.measurement.2016.10.009 |
| [6] | Zhang, C., Huang, Z., Ang, M.H., et al. (2021) Lidar Degradation Quantification for Autonomous Driving in Rain. 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Prague, 27 September-1 October 2021, 3458-3464. https://doi.org/10.1109/IROS51168.2021.9636694 |
| [7] | Goodin, C., Carruth, D., Doude, M., et al. (2019) Predict-ing the Influence of Rain on LIDAR in ADAS. Electronics, 8, Article 89. https://doi.org/10.3390/electronics8010089 |
| [8] | 邢星宇, 黄安, 姜为, 等. 降雨条件下车载激光雷达感知局限性[J]. 同济大学学报(自然科学版), 2023, 51(5): 785-793. |
| [9] | Li, Y., Duthon, P., Colomb, M., et al. (2020) What Happens for a ToF LiDAR in Fog? IEEE Transactions on Intelligent Transportation Systems, 22, 6670-6681. https://doi.org/10.1109/TITS.2020.2998077 |
| [10] | Kutila, M., Pyyk？nen, P., Holzhüter, H., et al. (2018) Automotive LiDAR Performance Verification in Fog and Rain. 2018 21st International Conference on Intelligent Transportation Systems (ITSC), Maui, 4-7 November 2018, 1695-1701. https://doi.org/10.1109/ITSC.2018.8569624 |
| [11] | Yang, T., Li, Y., Ruichek, Y., et al. (2021) Performance Modeling a Near-Infrared Tof Lidar under Fog: A Data-Driven Ap-proach. IEEE Transactions on Intelligent Transportation Systems, 23, 11227-11236.
https://doi.org/10.1109/TITS.2021.3102138 |
| [12] | Schlager, B., Goelles, T., Muckenhuber, S. and Watzenig, D. (2022) Contaminations on Lidar Sensor Covers: Performance Degradation Including Fault Detection and Modeling as Potential Applications. IEEE Open Journal of Intelligent Transportation Systems, 3, 738-747. https://doi.org/10.1109/OJITS.2022.3214094 |
| [13] | Rivero, J.R.V., Tahiraj, I., Schubert, O., et al. (2017) Character-ization and Simulation of the Effect of Road Dirt on the Performance of a Laser Scanner. 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), Yokohama, 16-19 October 2017, 1-6. https://doi.org/10.1109/ITSC.2017.8317784 |
| [14] | Schlager, B., Goelles, T. and Watzenig, D. (2021) Effects of Sensor Cover Damages on Point Clouds of Automotive Lidar. 2021 IEEE Sensors, Sydney, 31 October-3 November 2021, 1-4.
https://doi.org/10.1109/SENSORS47087.2021.9639697 |
| [15] | Trierweiler, M., Caldelas, P., Gr？ninger, G., et al. (2019) Influence of Sensor Blockage on Automotive LiDAR Systems. 2019 IEEE Sensors, Montreal, 27-30 October 2019, 1-4. https://doi.org/10.1109/SENSORS43011.2019.8956792 |
| [16] | James, J.K., Puhlfürst, G., Golyanik, V., et al. (2018) Classification of Lidar Sensor Contaminations with Deep Neural Networks. Proceedings of the Computer Science in Cars Symposium (CSCS), Munich, 13-14 September 2018, 1-8. |
| [17] | Xia, S., Chen, D., Wang, R., Li, J. and Zhang, X. (2020) Geometric Primitives in LiDAR Point Clouds: A Review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 685-707.
https://doi.org/10.1109/JSTARS.2020.2969119 |
| [18] | B？rcs, A., Nagy, B. and Benedek, C. (2017) Instant Object Detection in Lidar Point Clouds. IEEE Geoscience and Remote Sensing Letters, 14, 992-996. https://doi.org/10.1109/LGRS.2017.2674799 |
| [19] | 姜海娇, 来建成, 王春勇, 等. 激光雷达的测距特性及其测距精度研究[J]. 中国激光, 2011, 38(5): 229-235. |
| [20] | Stanford Artificial Intelligence Laboratory (2018) Robotic Op-erating System. https://www.ros.org/ |
