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The dynamic factors in the environment will lead to the decline of cameralocalization accuracy due to the violation of the static environment assumptionof SLAM algorithm. Recently, some related works generally use the combinationof semantic constraints and geometric constraints to deal with dynamic objects,but problems can still be raised, such as poor real-time performance, easy totreat people as rigid bodies, and poor performance in low dynamic scenes. Inthis paper, a dynamic scene-oriented visual SLAM algorithm based on objectdetection and coarse-to-fine static probability named CFP-SLAM is proposed. Thealgorithm combines semantic constraints and geometric constraints to calculatethe static probability of objects, keypoints and map points, and takes them asweights to participate in camera pose estimation. Extensive evaluations showthat our approach can achieve almost the best results in high dynamic and lowdynamic scenarios compared to the state-of-the-art dynamic SLAM methods, andshows quite high real-time ability.

CFP-SLAM: A Real-time Visual SLAM Based on Coarse-to-Fine Probability in Dynamic Environments