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17. Research on unmanned vehicle obstacle avoidance technology based on LIDAR and depth camera fusion

Author

Qiu Hao, Chen Weifeng, Ji Aihong, and Hu Kai

Subjects
lidar, depth camera, raster map method, unmanned vehicle, obstacle avoidance technique, positional attitude, bayesian estimation, 78-02, Mathematics, QA1-939
Abstract

To address the problems of poor accuracy of traditional EKF algorithm in estimating the position of unmanned vehicles and the deficiencies in accuracy and map completeness of the traditional map building method with single-line LiDAR, this paper proposes a method to create fused raster maps realized with multi-source data. Firstly, the combined data of the inertial measurement unit and wheel encoder are corrected by adding the positional information output from the visual odometer using the error-state SLAM algorithm, and the local raster constructed by LiDAR and depth camera is fused frame by frame using the idea of Bayesian estimation to finally generate the fused global map. Then, a four-wheeled mobile unmanned vehicle with a LiDAR sensor and depth camera is selected as the experimental object, and dynamic environment avoidance simulation experiments are conducted to draw conclusions. The simulation experiment results show that when γ = 5.99, the algorithm generates a new local target point pg2 (17.49, 13.49) and the corresponding getaway path and finally guides the unmanned vehicle to the specified target point, verifying that the method in this paper can achieve the avoidance capability of the unmanned vehicle in the process of getting away from the newly emerged obstacles. This study uses the scanned data of LiDAR for the estimation of the real-time position of the unmanned vehicle to realize obstacle avoidance and path planning of the unmanned vehicle.

Published
2024
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22. Dual Antiplatelet Therapies and Causes in Minor Stroke or Transient Ischemic Attack: A Prespecified Analysis in the CHANCE-2 Trial

Author

Xie, Xuewei, Jing, Jing, Meng, Xia, Claiborne Johnston, S., Bath, Philip M., Li, Zixiao, Zhao, Xingquan, Liu, Liping, Wang, Yilong, Xu, Qin, Wang, Anxin, Jiang, Yong, Li, Hao, Wang, Yongjun, Wang, Yongjun, Johnston, S. Claiborne, Xu, Anding, Bath, Philip M. W., Dong, Qiang, Wang, Yongjun, Li, Hao, Meng, Xia, Jing, Jing, Xie, Xuewei, Lin, Jinxi, Simon, Tabassome, Danchin, Nicolas, Zhi, Helen, Peng, Bin, Fan, Dongsheng, Wang, David, Youn, Teddy, Adams, Harold P., Jr., Meng, Xia, Xie, Xuewei, Jing, Jing, Lin, Jinxi, Niu, Siying, Li, Hao, Pan, Yuesong, Wang, Anxin, Jiang, Yong, Jin, Aoming, Wu, Haibo, Shi, Wei, Wu, Haibo, Li, Jianhua, Wang, Yanxia, Wei, Yan, Liu, Haichao, Zhao, Jianhua, Han, Xinshen, Sheng, Baoying, Yang, Xiaofan, Jing, Ping, Chen, Jiechun, Huang, Panbing, Chen, Guofang, Feng, Yefang, Wu, Lie, Zhu, Runxiu, Liu, Peiru, Chang, Liguo, Yang, Xiafeng, Wang, Xinqiang, Zhao, Jinguo, Zhang, Wei, Cui, Zhigang, Wu, Xuemei, Huang, Hua, Liu, Qiuwu, Zhao, Xingquan, Cheng, Dongli, Zhu, Yulan, Wang, Lihua, Li, Guozhong, Zhang, Xiaoxuan, Xue, Wenjun, Chen, Guohua, Li, Haipeng, Deng, Ke, Liu, Jingyao, Liu, Yansong, Wei, Xiu’e, Wang, Dunjing, Feng, Juan, Wang, Baojun, Chen, Yongming, Wu, Bing, Chai, Xiangting, Shi, Fudong, Cai, Xueli, Yang, Hongqin, Zhang, Yundong, Wang, Xia, Wang, Yumei, Shi, Zhimin, Chen, Shengli, Chen, Yangmei, Zhou, Lichun, Hong, Weimin, Yin, Lufeng, Ren, Lijie, Ma, Jianhua, Lei, Yongqian, Wu, Minghua, Zeng, Guoyong, Wu, Lingfeng, Cao, Mingha, Guo, Cunju, Man, Honghao, Bai, Yu, Wu, Aimei, Huang, Xuerong, Zhang, Chunhua, Jiang, Xiaoling, Shao, Peining, Luo, Dexin, Cai, Zhiyou, Jia, Yi, Ma, Aijun, Bai, Ying, Gong, Xuhai, Liu, Jirong, Zhang, Meng, Xu, Anding, Tan, Hong, An, Yi, Wang, Zhimin, Zhao, Weili, Wu, Xiangbin, Gao, Wenwei, Gao, Yueqiang, Li, Xinyi, Wu, Jin, Wang, Feng, Du, Ganqin, Wu, Kaiyi, Wan, Hebin, Chu, Lan, Hong, Daojun, Cao, Hui, Xu, Bing, Liu, Jie, Zhang, Hao, Yang, Jie, Zhang, Guiru, Chen, Hong, Zhang, Jianling, Li, Xuemei, Zou, Yu’an, Wang, Yan, Wei, Xia, Wang, Jianlin, Chen, Weifeng, Liu, Chunfeng, Sui, Changbai, Li, Hongyan, Zhong, Hua, Wu, Aisheng, Fan, Jinqi, Ji, Qiuhong, Lin, Jinshen, Shi, Xueying, Kang, Guangming, Xie, Haiyang, Ma, Dan, Wang, Hongliang, Chen, Wei, Shi, Peng, Shangguan, Wen, Gu, Jun, Feng, Xiaopeng, Zhan, Qunling, Han, Xianwen, Cao, Hongyuan, Wei, Liang, Lu, Weifeng, Zheng, Chunling, Yang, Chunjie, Zhao, Min, Wu, Heng, Dong, Chunhua, He, Yongli, Yao, Shufang, Zhu, Xiangyang, Zhao, Zhong, Ye, Min, Cai, Guodong, Peng, Guangyao, Li, Hongfei, Zhang, Lujun, Zhang, Shoucheng, Zhou, Wei, Liu, Hong, Fan, Xuewen, Tan, Wei, Zhu, Renxiang, He, Tianhui, Wang, Qizhang, Zhang, Lifang, Yang, Jinsuo, Liu, Yuqing, Su, Shaohua, He, Li, Yu, Yadong, Wei, Zhang, Zhankui, Lin, Rong Qiu, Xue, Bin Qi, Ying, Sheng, Lei, Zheng Wu, Shi, Di Wu, Song, Liang Wu, Sheng, Qin, Ding, Xiao, Bo, Wang, Kai, Ting Zhang, Xi, and Jing Pang, Jin

Published
2023
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36. The Construction of Electronic Information Application Specialized Top Talent Cultivation System in the Background of 'Intelligent Manufacturing + Artificial Intelligence'

Author

Wang Feng, Xu Zhiming, Zou Jiajun, and Chen Weifeng

Subjects
Social Sciences
Abstract

With the continuous emergence of emerging technologies, new application technologies gradually have changed the trend of life and society as well as work, in this context, emerging professions are also constantly being declared by colleges and universities, in recent years, especially hot smart manufacturing and artificial intelligence is under this trend of continuous development and growth, many colleges and universities are actively declared, more and more colleges and universities of the deployment of such professions, the traditional professions have a tendency to weaken in the In this context, how to deepen the engineering education reform of electronic information, relying on the changes brought about by the new profession to improve themselves, build the new development trend of the application of specialized top talents training system, to promote the traditional professions in the transformation of education reform to create a first-class, we must see the heritage of the traditional professions, to play to their own strengths in combination with the new technology and new systems, the integration of new professions of the excellent experience, the establishment of the application of the specialized top talents training system can continue to be the best. Specialized top talents training system can continue to serve the regional economic development and industrial upgrading of talent needs.

Published
2024
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