<p>In visual simultaneous localization and mapping (SLAM), loop closure detection plays an irreplaceable role in eliminating cumulative errors, optimizing robot poses, and ensuring map consistency. Most loop closure detection algorithms adopt single feature or feature fusion to detect loop closures, which makes it difficult to ensure accuracy in environments with changing lighting or high-similarity scenes. In this work, image features and motion trajectories are combined to improve the effectiveness of loop closure detection via a staged detection method. First, histogram equalization is used to reduce the algorithm’s sensitivity to lighting. Then, LBP features are used to divide keyframes into multiple sequences, and the sequence where the loop closure candidate frame is located is determined according to the image feature matching results. Then, the most matched keyframe is searched in the sequence as a candidate loop closure. Finally, the true loop closure is confirmed by comparing the motion trajectory similarity to improve the algorithm’s adaptability to high-similarity scenes. The experimental results show that in different application scenarios, the proposed method can achieve good results in terms of precision, recall, area under the curve (AUC), and recall when the precision is 100%.</p>