The feature point method is the mainstream method to accomplish inter-frame estimation in visual Simultaneous Localization and Mapping (SLAM) methods, among which the Oriented FAST and Rotated BRIEF (ORB) feature-based method provides an equilibrium of accuracy as well as efficiency. However, the ORB algorithm is prone to clustering phenomena, and its unequal distribution of extracted feature points is not conducive to the subsequent camera tracking. To solve the above problems, this paper suggests an adaptive feature extraction algorithm that first constructs multiple-scale images using an adaptive Gaussian pyramid algorithm, calculates adaptive thresholds, and uses an adaptive meshing method for regional feature point detection to adapt to different scenes. The method uses Adaptive and Generic Accelerated Segment Test (AGAST) to speed up feature detection and the non-maximum suppression method to filter feature points. The feature points are then divided equally by a quadtree technique, and the orientation of those points is determined by an intensity centroid approach. Experiments were conducted on publicly available datasets, and the outcomes demonstrate the algorithm has good adaptivity and solves the problem of a large number of corner point clusters that may result from using manually set detection thresholds. The RMSE of the absolute trajectory error of SLAM applying this method on four sequences of TUM RGB-D datasets is decreased by 13.88% when compared with ORB-SLAM3. It is demonstrated that the algorithm provides high-quality feature points for subsequent image alignment, and the application to SLAM improves the reliability and accuracy of SLAM.

To achieve effective connection between each node of the Internet of Things (IoT), there is a great demand of precise positioning. The traditional firefly localization algorithm only considers part of the ranging information, and the positioning effect is not good. Therefore, an improved algorithm is proposed to take advantage of the ranging information between unknown nodes to achieve cooperative location. First, all ranging information is considered to construct a new objective function. The firefly optimization approach is then utilized to discover the best solution based on the new goal function. The starting location created by the random technique has a significant impact on the new algorithm’s localization performance, thus the traditional firefly localization method’s initial position is employed to increase cooperative localization performance. The simulation results demonstrate that the novel approach outperforms the classic firefly technique in terms of placement accuracy.