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Purpose To realize stable and precise localization in the dynamic environments, the authors propose a fast and robust visual odometry (VO) approach with a low-cost Inertial Measurement Unit (IMU) in this study. Design/methodology/approach The proposed VO incorporates the direct method with the indirect method to track the features and to optimize the camera pose. It initializes the positions of tracked pixels with the IMU information. Besides, the tracked pixels are refined by minimizing the photometric errors. Due to the small convergence radius of the indirect method, the dynamic pixels are rejected. Subsequently, the camera pose is optimized by minimizing the reprojection errors. The frames with little dynamic information are selected to create keyframes. Finally, the local bundle adjustment is performed to refine the poses of the keyframes and the positions of 3-D points. Findings The proposed VO approach is evaluated experimentally in dynamic environments with various motion types, suggesting that the proposed approach achieves more accurate and stable location than the conventional approach. Moreover, the proposed VO approach works well in the environments with the motion blur. Originality/value The proposed approach fuses the indirect method and the direct method with the IMU information, which improves the localization in dynamic environments significantly.

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Focus on the problem that the traditional loop closure detection algorithm is unstable and easy to fail in dynamic scene, an algorithm that can accurately detect the closed loop under the dynamic scene is proposed. First of all, the algorithm for distinguishing dynamic and static features based on scene flow is improved. Then, dynamic feature points are removed and clustering is performed. The TF-IDF entropy of each node of the image in the visual dictionary tree is used as the weight of the image in the visual words, and a score vector is constructed to describe the scene. Finally, the negative exponent power function is used to calculate the similarity of the two images.. After the final closed-loop confirmation, the final key frame with the current frame closed-loop is obtained. The experiment of the actual scene shows that the algorithm proposed in this paper can effectively detect the loop closure in the dynamic scene.

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