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Journal of Graphics ›› 2023, Vol. 44 ›› Issue (4): 747-754.DOI: 10.11996/JG.j.2095-302X.2023040747

• Image Processing and Computer Vision • Previous Articles     Next Articles

Research on real-time dense reconstruction for open road scene

LI Xin-li(), MAO Hao, WANG Wu, YANG Guo-tian   

  1. School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China
  • Received:2023-02-03 Accepted:2023-03-24 Online:2023-08-31 Published:2023-08-16
  • About author:First author contact:

    LI Xin-li (1973-), associate professor, Ph.D. Her main research interests cover pattern recognition, intelligent system and digital image processing. E-mail:lixinli@ncepu.edu.cn


In order to tackle the problems of inefficiency and inaccurate mapping prevalent in the field of intelligent driving, a two-stage dense mapping algorithm for outdoor open road scenes based on multi-sensor fusion was proposed. The proposed algorithm comprised an extrinsic parameter real-time calibration module and a mapping module. The former constructed constraints and optimized them based on typical semantic and geometric features in road scenes, achieving real-time online calibration of extrinsic parameters between sensors. The latter’s core was a two-stage incremental mapping algorithm that performed incremental coarse mapping and fine mapping for the entire scene and road face area, respectively. The rough mapping could guarantee the real-time performance of the algorithm, and fine mapping could achieve accurate restoration of road surface textures such as traffic signs. Experimental results in an outdoor open road scene demonstrated that the proposed algorithm could perform real-time dense mapping in outdoor large-scale scenes, with high accuracy and efficiency.

Key words: open road scene, real-time dense reconstruction, multi-sensor fusion, extrinsic calibration, incremental mapping

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