A New Visual Inertial Simultaneous Localization and Mapping (SLAM) Algorithm Based on Point and Line Features

In view of traditional point-line feature visual inertial simultaneous localization and mapping (SLAM) system, which has weak performance in accuracy so that it cannot be processed in real time under the condition of weak indoor texture and light and shade change, this paper proposes an inertial SLAM method based on point-line vision for indoor weak texture and illumination. Firstly, based on Bilateral Filtering, we apply the Speeded Up Robust Features (SURF) point feature extraction and Fast Nearest neighbor (FLANN) algorithms to improve the robustness of point feature extraction result. Secondly, we establish a minimum density threshold and length suppression parameter selection strategy of line feature, and take the geometric constraint line feature matching into consideration to improve the efficiency of processing line feature. And the parameters and biases of visual inertia are initialized based on maximum posterior estimation method. Finally, the simulation experiments are compared with the traditional tightly-coupled monocular visual–inertial odometry using point and line features (PL-VIO) algorithm. The simulation results demonstrate that the proposed an inertial SLAM method based on point-line vision for indoor weak texture and illumination can be effectively operated in real time, and its positioning accuracy is 22% higher on average and 40% higher in the scenario that illumination changes and blurred image.

Improved Point-Line Feature Based Visual SLAM Method for Complex Environments

Traditional visual simultaneous localization and mapping (SLAM) systems rely on point features to estimate camera trajectories. However, feature-based systems are usually not robust in complex environments such as weak textures or obvious brightness changes. To solve this problem, we used more environmental structure information by introducing line segments features and designed a monocular visual SLAM system. This system combines points and line segments to effectively make up for the shortcomings of traditional positioning based only on point features. First, ORB algorithm based on local adaptive threshold was proposed. Subsequently, we not only optimized the extracted line features, but also added a screening step before the traditional descriptor matching to combine the point features matching results with the line features matching. Finally, the weighting idea was introduced. When constructing the optimized cost function, we allocated weights reasonably according to the richness and dispersion of features. Our evaluation on publicly available datasets demonstrated that the improved point-line feature method is competitive with the state-of-the-art methods. In addition, the trajectory graph significantly reduced drift and loss, which proves that our system increases the robustness of SLAM.