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基于异构数据融合的SLAM研究综述
作者:
作者单位:

1.自动化学院;2.南京信息工程大学


Overview of SLAM Research Based on Heterogeneous Data Fusion
Author:
Affiliation:

1.Nanjing university of information science&2.technology

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    摘要:

    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。该技术使得机器人能够在没有GPS的条件下,在室内外场景中移动。然而仅依靠单一传感器的SLAM技术都有着各自的局限性,如激光SLAM不适用于周围存在大量动态物体的场景,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。因此,本文预测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、基于特征以及新颖的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,并对未来发展做出展望。

    Abstract:

    After decades of development, laser and visual SLAM technology has been relatively mature and widely used in military and civil fields. The technology allows robots to move in indoor and outdoor scenarios where GPS signals are scarce. However, SLAM technology relying only on a single sensor has its own limitations. For example,laser SLAM is not suitable for scenes with a large number of dynamic objects around it, and visual SLAM has poor robustness in low-texture environments. However, the fusion of the two technologies has great potential to learn from each other. Therefore, this paper predicts that SLAM technology combining laser and vision or even more sensors will be the mainstream direction in the future. This paper reviews the development of SLAM technology, analyzes the hardware information of LIDAR and camera, and gives some classical open-source algorithms and datasets. According to the algorithm used in the fusion sensor, the multi-sensor fusion scheme is introduced in detail from the perspective of traditional uncertainty based, feature-based and novel deep learning based. The excellent performance of multi-sensor fusion scheme in complex scenes is summarized, and the future development of the multi-sensor fusion scheme is prospected.

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周铖君,陈炜峰,尚光涛,王曦杨,徐崇辉,李振雄.基于异构数据融合的SLAM研究综述[J].南京信息工程大学学报,,():

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  • 收稿日期:2022-10-02
  • 最后修改日期:2023-11-19
  • 录用日期:2023-11-20

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