Dense mapping is an important part of mobile robot navigation and environmental understanding. Aiming to address the problem that Dense Surfel Mapping relies on the input of a common-view relationship, we propose a local map extraction strategy based on spatiotemporal consistency. The local map is extracted through the inter-frame pose observability and temporal continuity. To reduce the blurring of map fusion caused by the different viewing angles, a normal constraint is added to the map fusion and weight initialization. To achieve continuous and stable time efficiency, we dynamically adjust the parameters of superpixel extraction. The experimental results on the ICL-NUIM and KITTI datasets show that the partial reconstruction accuracy is improved by approximately 27–43%. In addition, the system achieves a greater than 15 Hz real-time performance using only CPU computation, which is improved by approximately 13%.

In scenes where there are lighting changes, localization may fail for visual SLAM due to feature point tracking failure. Thus, a feature point tracking method based on multi-condition constraints is proposed for visual SLAM. The proposed method tracks the feature points of optical flow from aspects such as the overall motion position of feature points, descriptor grayscale information, and spatial geometric constraints. First, to solve the problem of feature point mismatch in complex environments, we propose a feature point mismatch removal method that combines optical flow, descriptor, and RANSAC. We eliminate incorrect feature point matches layer by layer through these constraints. The uniformity of feature point distribution in the image can then affect the accuracy of camera pose estimation, and different scenes can also affect the difficulty of feature point extraction. In order to balance the quality and uniformity of the extracted feature points, we propose an adaptive mask homogenization method that adaptively adjusts the mask radius according to the quality of feature points. Experiments conducted on the EuRoC dataset show that the proposed method which integrates the improved feature point mismatch removal method and mask homogenization method into feature point tracking, exhibits robustness and accuracy under various interferences such as lighting changes, image blurring, and unclear textures. Compared to the RANSAC method, we reduce the location error by about 85% using the EuRoC dataset.