Research on SLAM Algorithm of Mobile Robot Based on the Fusion of 2D LiDAR and Depth Camera

Abstract

This paper proposes a new Simultaneous Localization and Mapping (SLAM) method on the basis of graph-based optimization through the combination of the Light Detection and Ranging (LiDAR), RGB-D camera, encoder and Inertial Measurement Unit (IMU). It can conduct joint positioning of four sensors by taking advantaging of the unscented Kalman filter (UKF) to design the related strategy of the 2D LiDAR point cloud and RGB-D camera point cloud. 3D LiDAR point cloud information generated by the RGB-D camera under the 2D LiDAR has been added into the new SLAM method in the sequential registration stage, and it can match the 2D LiDAR point cloud and the 3D RGB-D point cloud by using the method of the Correlation Scan Matching (CSM); In the loop closure detection stage, this method can further verify the accuracy of the loop closure after the 2D LiDAR matching by describing 3D point cloud.Additionally, this new SLAM method has been verified feasibility and availability through the processes of theoretical derivation, simulation experiment and physical verification. As a result, the experiment shows that the multi-sensor SLAM framework designed has a good mapping effect, high precision and accuracy.

Adaptive Fuzzy Output Feedback Simultaneous Posture Stabilization and Tracking Control of Wheeled Mobile Robots With Kinematic and Dynamic Disturbances

“…Simulation results using the proposed method for the WMR with disturbances are presented. The kinematics are provided by (20), in which the kinematic disturbances are set to In addition, the dynamics in (39) are used instead of the ones in (37) through the use of computed-torque control in (38) ξ ω = cos( ),1] T t . For nonlinear and adaptive fuzzy control methods, the design parameters should be chosen through trial and error by observing the estimation and control performance.…”

“…In addition to the kinematic disturbances, dynamic disturbances are assumed to exist in the WMR system as [ , ] T f f = are estimates of the dynamic disturbances obtained later in (44). Substition of (40) into (39) gives Because a lateral slipping appears as w δ that is dependent on v and ω , the state-dependent dynamic disturbances are assumed to be included in the form of f as in (39). Therefore, the control input u in (40) should be designed using the velocity estimates obtained from the velocity observer in (29), and the disturbance estimates obtained from the adaptive fuzzy disturbance observer designed below in (42)- (45).…”

“…By combining (48) θ , the tracking errors need to be analyzed by considering the dynamics in (41) and the adaptive fuzzy disturbance observer in (42)- (45). The use of the adaptive fuzzy disturbance observer for the dynamic disturbances present in (39) guarantees that the disturbance estimation errors are ultimately bounded and the ultimate bounds can be adjusted by w d as in [18]. Therefore, it can be analyzed that the overall tracking errors of the AFOFTC system also become ultimately bounded and can be adjusted through w d .…”

“…Simulation and experimental results are also provided to demonstrate the effectiveness of the proposed method. Considering the practical applicability of the WMR [39]- [43], the actual WMR system is used in the experiments to demonstrate that the complexity and implementation issues of the proposed method do not cause a problem, and it is demonstrated that it is possible to control the overall WMR system using the proposed method in real time.…”

“…increasing the number of the fuzzy rules.On the basis of the dynamics with the disturbances in(39), an adaptive fuzzy disturbance observer with the velocity estimates is designed for the estimation of 1 gains v γ , w γ , and ε γ . The stability of the overall WMR control system using the proposed AFOFTC law is analyzed in Theorem 3.Theorem 3 (Reference tracking mode using AFOFTC):Under Assumption 1, the use of the AFOFTC input in(38),(40), (42)-(45) guarantees the asymptotic tracking performance of the WMR control system against the kinematic and dynamic uncertainties such that posture tracking errors are uniformly bounded and their ultimate bounds can be adjusted by the dead-zone width w d…”