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In dynamic scenarios, dynamic participants break the static assumptions of the visual odometry (VO) algorithm. Hence, dynamic participants are typically removed and only static participants are used as motion references. When semantic information is used to eliminate participants with dynamic semantic labels in the scene, the dynamic degree of the scene affects system robustness and poses estimation accuracy. In this paper, we propose a VO system that employs adaptive compensation for semantic information and utilizes an adaptive compensation model for sparse static feature map construction to make the compensation process a flexible, independent thread. Moreover, a layered extraction fusion framework is proposed to uniformly sample equi-probability feature points both globally and locally. This framework combines multi-level weights of scene mapping and spatial-temporal priority information of self-organization. Finally, in the candidate pixel extraction stage, system efficiency is improved through region matching candidate pixel detection, extraction and fusion semantic constraints. Experiments are done by using the TUM RGBD datasets. Comparisons with many conventional visual mileage calculation methods reveal that the absolute and relative trajectory errors of camera motion are significantly reduced in most scenes with different dynamic degrees. Thus, compared to previous algorithms, the proposed algorithm is more robust and precise in dynamic scenes.

Traditional approaches to modeling and processing discrete pixels are mainly based on image features or model optimization. These methods often result in excessive shrinkage or expansion of the restored pixel region, inhibiting accurate recovery of the target pixel region shape. This paper proposes a simultaneous source and mask-images optimization model based on skeleton divergence that overcomes these problems. In the proposed model, first, the edge of the entire discrete pixel region is extracted through bilateral filtering. Then, edge information and Delaunay triangulation are used to optimize the entire discrete pixel region. The skeleton is optimized with the skeleton as the local optimization center and the source and mask images are simultaneously optimized through edge guidance. The technique for order of preference by similarity to ideal solution (TOPSIS) and point-cloud regularization verification are subsequently employed to provide the optimal merging strategy and reduce cumulative error. In the regularization verification stage, the model is iteratively simplified via incremental and hierarchical clustering, so that point-cloud sampling is concentrated in the high-curvature region. The results of experiments conducted using the moving-target region in the RGB-depth (RGB-D) data (Technical University of Munich, Germany) indicate that the proposed algorithm is more accurate and suitable for image processing than existing high-performance algorithms.

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