In this letter, we present an innovative, tightly-coupled LiDAR-Visual-Inertial Odometry and Mapping framework, termed LVIO-Fusion, which achives robust and precise state estimation and map construction in environments characterized by LiDAR-degenerated and texture-less. The LVIO-Fusion is comprised of two subsystems: a LiDAR-inertial odometry (LIO) and visual-inertial odometry (VIO). The LIO subsystem employs a dynamic voxel mapping method by utilizing a Hash table and octrees for building and updating point cloud map directly and efficiently. The map points are directly projected to images to build the optical flow matching, which couples LiDAR and camera measurements in a deeper level. Then, the VIO subsystem proposes a coarse-to-fine state estimation that minimizes the frame-to-frame reprojection errors and photometric errors to align images. Furthermore, the VIO subsystem leverages online photometric calibration to acquire the true radiometric information from the environment, consequently refining the precision of the system. We extensively evaluate our method in challenge environments. The results validate the high accuracy and robustness of our method when compared to other state-of-the-art methods in LiDAR-degenerated and texture-less scenarios.

Accurate dynamic model is essential for the model-based control of robotic systems. However, on the one hand, the nonlinearity of the friction is seldom treated in robot dynamics. On the other hand, few of the previous studies reasonably balance the calculation time-consuming and the quality for the excitation trajectory optimization. To address these challenges, this article gives a Lie-theory-based dynamic modeling scheme of multi-degree-of-freedom (DoF) serial robots involving nonlinear friction and excitation trajectory optimization. First, we introduce two coefficients to describe the Stribeck characteristics of Coulomb and static friction and consider the dependency of friction on load torque, so as to propose an improved Stribeck friction model. Whereafter, the improved friction model is simplified in a no-load scenario, a novel nonlinear dynamic model is linearized to capture the features of viscous friction across the entire velocity range. Additionally, a new optimization algorithm of excitation trajectories is presented considering the benefits of three different optimization criteria to design the optimal excitation trajectory. On the basis of the above, we retrieve a feasible dynamic parameter set of serial robots through the hybrid least square algorithm. Finally, our research is supported by simulation and experimental analyses of different combinations on the seven-DoF Franka Emika robot. The results show that the proposed friction has better accuracy performance, and the modified optimization algorithm can reduce the overall time required for the optimization process while maintaining the quality of the identification results.

Continuum robots have good adaptability in unstructured and complex environments. However, affected by their inherent nature of flexibility and slender structure, there are challenges in high-precision motion and load. Thus, stiffness adjustment for continuum robots has consistently attracted the attention of researchers. In this paper, a stiffness adjustment mechanism (SAM) is proposed and built in a growth-controllable continuum robot (GCCR) to improve the motion accuracy in variable scale motion. The self-stiffness adjustment is realized by antagonism through cable force transmission during the length change of the continuum robot. With a simple structure, the mechanism has a scarce impact on the weight and mass distribution of the robot and required no independent actuators for stiffness adjustment. Following this, a static model considering gravity and end load is established. The presented theoretical static model is applicable to predict the shape deformations of robots under different loads. The experimental validations showed that the maximum error ratio is within 5.65%. The stiffness of the robot can be enhanced by nearly 79.6%.

The detection and assessment of pipeline damage constitute crucial steps in pipeline maintenance. Typically, internal pipeline robots extract and track feature points under actively illuminated conditions to accomplish state estimation. However, unstable lighting within pipelines can lead to poor feature tracking performance, resulting in localization divergence. In this paper, we propose a multi-feature tracking approach that, in addition to extracting a certain number of Shi-Tomasi corners, also extracts highly efficient FAST corners to maintain feature uniqueness and expedite extraction efficiency. Moreover, we introduce a bidirectional optical flow method that performs reverse optical flow tracking on top of the existing forward tracking, thereby eliminating outliers from failed tracking and enhancing the stability of optical flow tracking. A segmented optimization approach is used during the back-end optimization process to conserve computational resources and speed up the optimization process for the variables. The proposed method is integrated into the state-of-the-art visual-inertial SLAM system, VINS-Mono, and evaluated on public datasets and pipeline datasets. Compared to other methods, our approach demonstrates matching or superior performance in terms of localization accuracy and robustness.

The accurate estimation of the end-effector load force is essential in dynamic robotic scenarios, especially when the end-effector payload varies, to ensure safe and stable physical interaction among humans, robots, and environments. Currently, most applications still rely on payload calibration schemes, but existing calibration techniques often struggle to balance efficiency and accuracy. Moreover, current-based payload estimation methods, which are a commonly used and low-cost technique, face practical challenges such as non-negligible noise. To handle these issues, we propose a sensorless scheme based on a modified disturbance Kalman filter for accurately estimating the load force exerted on robots. Specifically, we introduce the dynamic model of robots that incorporates the nonlinear friction related to velocity and load. Subsequently, a generalized disturbance observer for the robot dynamics is adopted to avoid the measurement noise of joint acceleration. Considering the influence of friction and velocity on the noise parameters in the Kalman filter, a variable-parameter noise model is established. Finally, experimental results demonstrate that the proposed method achieves better performance in terms of accuracy, response, and overshoot suppression compared to the existing methods.

Uncertainty awareness in geometric observations is crucial for LiDAR-based simultaneous localization and mapping (SLAM) since it determines the direction of optimization and ultimately affects the accuracy of localization. In this paper, we present a tightly-coupled LiDAR-inertial odometry system with geometric uncertainty modeling framework for accurate and robust state estimation. The proposed LIO system is designed by an Iterative Error State Kalman Filter (IESKF) to effectively fuse LiDAR and IMU measurement, and employs the voxel structure to query and update LiDAR features. To observe the reliability of geometric measurements, we propose novel uncertainty models of LiDAR point, line and plane features, and apply them to construct point-to-line and point-to-plane constraints. In the maximization of posterior state estimation, we propagate the uncertainties of geometric constraints to the iterative error state Kalman filter, further consider the weights of geometric constraints, and enrich the comprehensiveness of the geometric residuals. To reduce the memory consumption of the system, we propose a cumulative update method with uncertainty parameters to avoid repeated parameter computations. We extensively evaluate our method in diverse public datasets. The results validate the high precision and robustness of the proposed method when compared to other state-of-the-art methods in challenge scenes with high perceptual uncertainties.