基于异构数据融合的SLAM研究综述

Overview of SLAM research based on heterogeneous data fusion

激光与视觉SLAM技术经过几十年的发展,目前都已经较为成熟,并被广泛应用于军事和民用领域.单一传感器的SLAM技术都存在局限性,如激光SLAM不适用于周围存在大量动态物体的场景,而视觉SLAM在低纹理环境中鲁棒性差,但两者融合使用具有巨大的取长补短的潜力,激光与视觉甚至是更多传感器融合的SLAM技术将会是未来的主流方向.本文回顾了SLAM技术的发展历程,分析了激光雷达与视觉的硬件信息,给出了一些经典的开源算法与数据集.根据融合传感器所使用的算法,从传统基于不确定度、基于特征以及基于深度学习的角度详细介绍了多传感器融合方案,概述了多传感器融合方案在复杂场景中的优异性能,并对未来发展作出了展望.

Laser SLAM(Simultaneous Localization and Mapping)and visual SLAM have been fully developed and widely used in military and civil fields.However,single sensor SLAM has limitations,for instance,laser SLAM is not suitable for scenes with a large number of dynamic objects around it,while visual SLAM has poor robustness in low-texture environments.Therefore,fusion of the two technologies has great potential to compensate each other,and it can be prospected that SLAM technology combining laser and vision or even more sensors will be the mainstream direction in the future.Here,we review the development of SLAM technology,analyze the hardware information of lidar and camera,and introduce some classical open-source algorithms and datasets.Furthermore,the multi-sensor fusion schemes are detailed from perspectives of uncertainty,feature and novel deep learning.The excellent performance of multi-sensor fusion schemes in complex scenes are summarized,and the future development trend of multi-sensor fusion is prospected.