Application of Kalman Filter to Improve 3D LiDAR Signals of Autonomous Vehicles in Adverse Weather
A worldwide increase in the number of vehicles on the road has led to an increase in the frequency of serious traffic accidents, causing loss of life and property. Autonomous vehicles could be part of the solution, but their safe operation is dependent on the onboard LiDAR (light detection and rangi...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/7/3018 |
| _version_ | 1827696575501041664 |
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| author | Shih-Lin Lin Bing-Han Wu |
| author_facet | Shih-Lin Lin Bing-Han Wu |
| author_sort | Shih-Lin Lin |
| collection | DOAJ |
| description | A worldwide increase in the number of vehicles on the road has led to an increase in the frequency of serious traffic accidents, causing loss of life and property. Autonomous vehicles could be part of the solution, but their safe operation is dependent on the onboard LiDAR (light detection and ranging) systems used for the detection of the environment outside the vehicle. Unfortunately, problems with the application of LiDAR in autonomous vehicles remain, for example, the weakening of the echo detection capability in adverse weather conditions. The signal is also affected, even drowned out, by sensory noise outside the vehicles, and the problem can become so severe that the autonomous vehicle cannot move. Clearly, the accuracy of the stereo images sensed by the LiDAR must be improved. In this study, we developed a method to improve the acquisition of LiDAR data in adverse weather by using a combination of a Kalman filter and nearby point cloud denoising. The overall LiDAR framework was tested in experiments in a space 2 m in length and width and 0.6 m high. Normal weather and three kinds of adverse weather conditions (rain, thick smoke, and rain and thick smoke) were simulated. The results show that this system can be used to recover normal weather data from data measured by LiDAR even in adverse weather conditions. The results showed an effective improvement of 10% to 30% in the LiDAR stereo images. This method can be developed and widely applied in the future. |
| first_indexed | 2024-03-10T12:50:29Z |
| format | Article |
| id | doaj.art-8de5ecc596764959ad9cffeb96708566 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T12:50:29Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-8de5ecc596764959ad9cffeb967085662023-11-21T13:07:35ZengMDPI AGApplied Sciences2076-34172021-03-01117301810.3390/app11073018Application of Kalman Filter to Improve 3D LiDAR Signals of Autonomous Vehicles in Adverse WeatherShih-Lin Lin0Bing-Han Wu1Graduate Institute of Vehicle Engineering, National Changhua University of Education, No.1, Jin-De Rd., Changhua City, Changhua 500, TaiwanGraduate Institute of Vehicle Engineering, National Changhua University of Education, No.1, Jin-De Rd., Changhua City, Changhua 500, TaiwanA worldwide increase in the number of vehicles on the road has led to an increase in the frequency of serious traffic accidents, causing loss of life and property. Autonomous vehicles could be part of the solution, but their safe operation is dependent on the onboard LiDAR (light detection and ranging) systems used for the detection of the environment outside the vehicle. Unfortunately, problems with the application of LiDAR in autonomous vehicles remain, for example, the weakening of the echo detection capability in adverse weather conditions. The signal is also affected, even drowned out, by sensory noise outside the vehicles, and the problem can become so severe that the autonomous vehicle cannot move. Clearly, the accuracy of the stereo images sensed by the LiDAR must be improved. In this study, we developed a method to improve the acquisition of LiDAR data in adverse weather by using a combination of a Kalman filter and nearby point cloud denoising. The overall LiDAR framework was tested in experiments in a space 2 m in length and width and 0.6 m high. Normal weather and three kinds of adverse weather conditions (rain, thick smoke, and rain and thick smoke) were simulated. The results show that this system can be used to recover normal weather data from data measured by LiDAR even in adverse weather conditions. The results showed an effective improvement of 10% to 30% in the LiDAR stereo images. This method can be developed and widely applied in the future.https://www.mdpi.com/2076-3417/11/7/3018autopilotLiDAR of autonomous vehiclesKalman filter |
| spellingShingle | Shih-Lin Lin Bing-Han Wu Application of Kalman Filter to Improve 3D LiDAR Signals of Autonomous Vehicles in Adverse Weather Applied Sciences autopilot LiDAR of autonomous vehicles Kalman filter |
| title | Application of Kalman Filter to Improve 3D LiDAR Signals of Autonomous Vehicles in Adverse Weather |
| title_full | Application of Kalman Filter to Improve 3D LiDAR Signals of Autonomous Vehicles in Adverse Weather |
| title_fullStr | Application of Kalman Filter to Improve 3D LiDAR Signals of Autonomous Vehicles in Adverse Weather |
| title_full_unstemmed | Application of Kalman Filter to Improve 3D LiDAR Signals of Autonomous Vehicles in Adverse Weather |
| title_short | Application of Kalman Filter to Improve 3D LiDAR Signals of Autonomous Vehicles in Adverse Weather |
| title_sort | application of kalman filter to improve 3d lidar signals of autonomous vehicles in adverse weather |
| topic | autopilot LiDAR of autonomous vehicles Kalman filter |
| url | https://www.mdpi.com/2076-3417/11/7/3018 |
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