To address the issues of low trajectory tracking accuracy and difficulties in tuning control parameters for crawler robots operating in uneven terrains, this paper proposes a trajectory tracking control method. The method is based on improved particle swarm optimization and sliding mode active disturbance rejection control (SPSO-SMADRC). Firstly, considering the influence of disturbances such as terrain undulations and soil inhomogeneity on trajectory deviation, the kinematic and dynamic models of the crawler robot are established. A vector field guidance approach is employed to transform the trajectory tracking task into a heading control problem. The heading angle is adaptively adjusted based on the position deviation and path curvature. A nonlinear extended state observer is introduced to estimate external disturbances. A velocity-based SMADRC controller is designed to dynamically regulate the robot’s linear and angular velocities. This allows real-time correction of the robot’s motion. To overcome the tendency of the standard particle swarm optimization (PSO) algorithm to fall into local optima during controller parameter tuning, a nonlinear dynamic adjustment strategy was adopted. This strategy adaptively adjusts the inertia weight and learning factors, enhancing the algorithm’s global search capability. Comparative experiments were conducted using two types of curved trajectories: U-shaped and V-shaped paths. The experimental results show that, under the proposed SPSO-SMADRC method, the crawler robot achieved maximum position errors of 8.28 cm and 9.26 cm, average position errors of 1.41 cm and 2.94 cm, and maximum heading angle deviations of 0.56 rad and 0.87 rad. The standard deviations of the position errors were 3.19 and 4.28, respectively. Compared with conventional PSO-based SMADRC and standard SMADRC methods, the proposed approach improved the navigation tracking accuracy. In the U-shaped trajectory, the maximum position error was reduced by 19.22% and 38.21%, the average position error by 40.00% and 65.53%, and the heading angle error by 28.21% and 74.66%. In the V-shaped trajectory, the maximum position error was reduced by 17.39% and 38.95%, the average position error by 51.71% and 52.04%, and the heading angle error by 80.58% and 84.49%. These results demonstrate that the proposed SPSO-SMADRC method significantly enhances trajectory tracking performance and system robustness. It provides effective support for high-precision autonomous navigation of crawler robots in complex and unstructured environments.

Visual SLAM relies on the motion information of static feature points in keyframes for both localization and map construction. Dynamic feature points interfere with inter-frame motion pose estimation, thereby affecting the accuracy of map construction and the overall robustness of the visual SLAM system. To address this issue, this paper proposes a method for eliminating feature mismatches between frames in visual SLAM under dynamic scenes. First, a spatial clustering-based RANSAC method is introduced. This method eliminates mismatches by leveraging the distribution of dynamic and static feature points, clustering the points, and separating dynamic from static clusters, retaining only the static clusters to generate a high-quality dataset. Next, the RANSAC method is introduced to fit the geometric model of feature matches, eliminating local mismatches in the high-quality dataset with fewer iterations. The accuracy of the DSSAC-RANSAC method in eliminating feature mismatches between frames is then tested on both indoor and outdoor dynamic datasets, and the robustness of the proposed algorithm is further verified on self-collected outdoor datasets. Experimental results demonstrate that the proposed algorithm reduces the average reprojection error by 58.5% and 49.2%, respectively, when compared to traditional RANSAC and GMS-RANSAC methods. The reprojection error variance is reduced by 65.2% and 63.0%, while the processing time is reduced by 69.4% and 31.5%, respectively. Finally, the proposed algorithm is integrated into the initialization thread of ORB-SLAM2 and the tracking thread of ORB-SLAM3 to validate its effectiveness in eliminating feature mismatches between frames in visual SLAM.