Keyframe-based RGB-D dense visual SLAM fused semantic cues in dynamic scenes

The robustness of dense visual SLAM is still a challenging problem in dynamic environments. In this paper, we propose a novel keyframe-based dense visual SLAM to handle a highly dynamic environment by using an RGB-D camera. The proposed method uses cluster-based residual models and semantic cues to detect dynamic objects, resulting in motion segmentation that outperforms traditional methods. The method also employs motion-segmentation based keyframe selection strategies and frame-to-keyframe matching scheme that reduce the influence of dynamic objects, thus minimizing trajectory errors. We further filter out dynamic object influence based on motion segmentation and then employ true matches from keyframes, which are near the current keyframe, to facilitate loop closure. Finally, a pose graph is established and optimized using the g2o framework. Our experimental results demonstrate the success of our approach in handling highly dynamic sequences, as evidenced by the more robust motion segmentation results and significantly lower trajectory drift compared to several state-of-the-art dense visual odometry or SLAM methods on challenging public benchmark datasets.