Loop closure detection is essential for simultaneous localization and mapping systems to decrease accumulating drift of trajectory estimations. Robust loop closure detection is specifically important in large-scale mapping, but it gets more challenging as the mapping environment grows. This paper proposes a SLAM system utilizing a two-pass loop closure detection method to improve mapping accuracy in large-scale environments. The proposed system finds loop closure candidates by employing global and local image features together. After selecting a group of candidates by similarity of global features, the system applies keypoint matching on this group to improve scene matching accuracy and determines loop closure candidates. We extensively evaluate the system on the widely used TUM RGB-D dataset, which contains sequences of small to large-scale indoor environments, with respect to different parameter combinations. The results show that the proposed method increases accuracy substantially and achieves large-scale mapping with acceptable overhead.

中文翻译：

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整合全局和局部图像特征以增强 RGB-D SLAM 系统中的回环检测

闭环检测对于同时定位和映射系统至关重要，以减少轨迹估计的累积漂移。稳健的回环检测在大规模映射中尤为重要，但随着映射环境的增长，它变得更具挑战性。本文提出了一种 SLAM 系统，该系统利用两次循环闭环检测方法来提高大规模环境中的映射精度。所提出的系统通过同时使用全局和局部图像特征来找到闭环候选。系统通过全局特征的相似度选择一组候选后，对该组进行关键点匹配，以提高场景匹配精度，确定回环候选。我们在广泛使用的 TUM RGB-D 数据集上广泛评估了系统，其中包含小规模到大规模室内环境的序列，相对于不同的参数组合。结果表明，所提出的方法显着提高了精度，并以可接受的开销实现了大规模映射。