基于3D激光雷达的SLAM算法研究现状与发展趋势

即时定位与地图构建(simultaneous localization and mapping, SLAM)算法是移动机器人实现自主移动的关键环节。激光雷达(LiDAR)具有测距精度高、不易受外部干扰和地图构建直观方便等优点，广泛应用于大型复杂室内外场景地图的构建。随着3D激光器的应用与普及，国内外学者围绕基于3D激光雷达的SLAM算法的研究已取得丰硕的成果。梳理了3D激光SLAM算法在前端数据关联、后端优化等环节的国内外研究现状，分析总结了目前各种3D激光SLAM算法以及改进方案的原理和优缺点，阐述了深度学习和多传感器融合理论与技术在3D激光SLAM算法中的应用情况，指出多源信息融合、与深度学习结合、应用场景的鲁棒性、 SLAM算法通用框架及移动传感器和无线信号体制的技术渗透是3D激光SLAM算法的研究热点和发展趋势。研究成果对3D激光SLAM算法和未知环境中移动机器人即时定位和地图构建的研究具有重要的参考价值和指导意义。

State-of-the-art and Tendency of SLAM Algorithms Based on 3D LiDAR

The simultaneous localization and mapping (SLAM) algorithm is the key link in achieving the autonomous mobility of mobile robots. Light detection and ranging (LiDAR) has the advantages of high range accuracy, less susceptibility to external interference, and intuitive and convenient map construction, and is widely used in map construction for large and complex indoor and outdoor scenes. Domestic and foreign scholars have achieved fruitful results in the research of SLAM algorithms based on 3D LiDAR due to the application and popularity of 3D lasers. The current status of domestic and foreign research on 3D laser SLAM algorithms in front-end data association, back-end optimization, etc., and the principles and advantages and disadvantages of various 3D laser SLAM algorithms and improvement schemes are analyzed and summarized in combination with deep learning and multi-sensor fusion. The application of theories and technologies in 3D laser SLAM algorithms is described, and research hotspots and development trends of 3D laser SLAM algorithms are highlighted, including multi-source information fusion, integration with deep learning, the robustness of application scenarios, a generic framework for SLAM algorithms, and technology penetration of mobile sensors and wireless signal regimes. The research results have significant reference value and guiding significance for the research of 3D laser SLAM algorithms and instant localization and map construction of mobile robots in unknown environments.