The present prevailing loop closure detection algorithm is mainly applicable for simultaneous localization and mapping (SLAM). Its effectiveness is contingent upon environmental conditions, which can fluctuate due to variations in lighting or the surrounding scenario. Vision-based algorithms, while adept during daylight hours, may falter in nocturnal settings. Conversely, lidar methods hinge on the sparsity of the given scenario. This paper proposes an algorithm that comprehensively utilizes lidar and image features to assign weighted factors for loop closure detection based on multi-modal sensor fusion. First, we use $k$-means clustering to produce a point cloud spatial global bag of words. Second, an improved deep learning method is used to train feature descriptors of images while scan context is also used to detect candidate point cloud features. After that, different feature-weighted factors are assigned for homologous feature descriptors. Finally, the detection result related to the maximum weight factor is designated to the optimal loop closure. The adaptive weighted loop closure (AWLC) algorithm we proposed inherits the advantages of different loop closure detection algorithms and hence it is accurate and robust. The AWLC method is compared with popular loop detection algorithms in different datasets. Experiments show that the AWLC can maintain the effectiveness and robustness of detection even at night or in highly dynamic complex environment.

目前流行的闭环检测算法主要适用于同步定位与建图（SLAM）。其有效性取决于环境条件，环境条件可能因照明或周围场景的变化而波动。基于视觉的算法虽然在白天很有效，但在夜间环境中可能会出现问题。相反，激光雷达方法取决于给定场景的稀疏性。本文提出一种综合利用激光雷达和图像特征为基于多模态传感器融合的闭环检测分配权重因子的算法。首先，我们使用$k$-意味着聚类产生点云空间全局词袋。其次，使用改进的深度学习方法来训练图像的特征描述符，同时扫描上下文也用于检测候选点云特征。之后，为同源特征描述符分配不同的特征加权因子。最后，将与最大权重因子相关的检测结果指定为最优闭环。我们提出的自适应加权闭环（AWLC）算法继承了不同闭环检测算法的优点，因此准确且鲁棒。在不同的数据集中将 AWLC 方法与流行的循环检测算法进行了比较。实验表明，即使在夜间或高度动态的复杂环境下，AWLC也能保持检测的有效性和鲁棒性。