Most existing vision-based simultaneous localization and mapping (SLAM) systems and their variants still assume that the observation is absolutely static and cannot work well in dynamic environments. Here, we present the Dynam-SLAM (Dynam), a stereo visual-inertial SLAM system capable of robust, accurate, and continuous work in high dynamic environments. Our approach is devoted to loosely coupling the stereo scene flow with an inertial measurement unit (IMU) for dynamic feature detection and tightly coupling the dynamic and static features with the IMU measurements for nonlinear optimization. First, the scene flow uncertainty caused by measurement noise is modeled to derive the accurate motion likelihood of landmarks. Meanwhile, to cope with highly dynamic environments, we additionally construct the virtual landmarks based on the detected dynamic features. Then, we build a tightly coupled, nonlinear optimization-based SLAM system to estimate the camera state by fusing IMU measurements and feature observations. Finally, we evaluate the proposed dynamic feature detection module (DFM) and the overall SLAM system in various benchmark datasets. Experimental results show that the Dynam is almost unaffected by DFM and performs well in static EuRoC datasets. Dynam outperforms the current state-of-the-art visual and visual-inertial SLAM implementations in terms of accuracy and robustness in self-collected dynamic datasets. The average absolute trajectory error of Dynam in the dynamic benchmark datasets is $\sim$90% lower than that of VINS-Fusion, $\sim$84% lower than that of ORB-SLAM3, and $\sim$88% lower than that of Kimera.

Motion capture of the human body potentially holds great significance for exoskeleton robots, human-computer interaction, sports analysis, rehabilitation research, and many other areas. Dielectric elastomer sensors (DESs) are excellent candidates for wearable human motion capture systems because of their intrinsic characteristics of softness, light weight, and compliance. In this paper, DESs were applied to measure all component motions of the wrist joints. Five sensors were mounted to different positions on the wrist, and each one is for one component motion. To find the best position to mount the sensors, the distribution of the muscles is analyzed. Even so, the component motions and the deformation of the sensors are coupled; therefore, a decoupling method was developed. By the decoupling algorithm, all component motions can be measured with a precision of 5°, which meets the requirements of general motion capture systems.

This letter introduces an efficient visual-inertial simultaneous localization and mapping (SLAM) method using point and line features. Currently, point-based SLAM methods do not perform well in scenarios such as weak textures and motion blur. Many researchers have noticed the excellent properties of line features in space and have attempted to develop line-based SLAM systems. However, the vast computational effort of the line extraction and description matching process makes it challenging to guarantee the real-time performance of the whole SLAM system, and the incorrect line detection and matching limit the performance improvement of the SLAM system. In this letter, we improve the traditional line detection model by means of short-line fusion, line feature uniform distribution, and adaptive threshold extraction to obtain high-quality line features for constructing SLAM constraints. Based on the gray level invariance assumption and colinear constraint, we propose a line optical flow tracking method, which significantly improves the speed of line feature matching. In addition, a measurement model that is independent of line endpoints is presented for estimating line residuals. The experimental results show that our algorithm improves the efficiency of line feature detection and matching and localization accuracy.

Existing robots capable of hopping or running mostly rely on rigid actuators, but soft hopping or running robots based on muscle-like actuators have not yet been achieved. In this article, we report a tethered soft robot capable of hopping–running on smooth and rough surfaces with high speed. The hopping–running robot is composed of two soft joints that simulate the foreleg and hind leg driven by dielectric elastomer. The mass, length, width, and height of the robot are 6.5 g, 8.5 cm, 4.8 cm, and 50 cm, respectively. The robot can run at a speed of 51.83 cm/s (6.10 body lengths/s), which is much faster than previously reported locomotion robots driven by soft responsive materials. The robot also shows good adaptability to different terrains, such as marble, wood, rubber, sandpaper, and slopes. The robot can carry a load equal to its weight, can maintain a high locomotion speed, and demonstrates the potential ability to carry its power supply and control circuitry.

Dielectric elastomer minimum energy structures (DEMES) can realize large angular deformations by small voltage-induced strains, which make them an attractive candidate for use as soft actuators. If the task only needs binary action, the bistable structure will be an efficient solution and can save energy because it requires only a very short duration of voltage to switch its state. To obtain bistable DEMES, a method to realize the two stable states of traditional DEMES is provided in this paper. Based on this, a type of symmetrical bistable DEMES is proposed, and the required actuation pulse duration is shorter than 0.1 s. When a suitable mass is attached to end of the DEMES, or two layers of dielectric elastomer are affixed to both sides of the primary frame, the DEMES can realize two stable states and can be switched by a suitable pulse duration. To calculate the required minimum pulse duration, a mathematical model is provided and validated by experiment.

The calibration problem of binocular stereo vision rig is critical for its practical application. However, most existing calibration methods are based on manual off-line algorithms for specific reference targets or patterns. In this paper, we propose a novel simultaneous localization and mapping (SLAM)-based self-calibration method designed to achieve real-time, automatic and accurate calibration of the binocular stereo vision (BSV) rig’s extrinsic parameters in a short period without auxiliary equipment and special calibration markers, assuming the intrinsic parameters of the left and right cameras are known in advance. The main contribution of this paper is to use the SLAM algorithm as our main tool for the calibration method. The method mainly consists of two parts: SLAM-based construction of 3D scene point map and extrinsic parameter calibration. In the first part, the SLAM mainly constructs a 3D feature point map of the natural environment, which is used as a calibration area map. To improve the efficiency of calibration, a lightweight, real-time visual SLAM is built. In the second part, extrinsic parameters are calibrated through the 3D scene point map created by the SLAM. Ultimately, field experiments are performed to evaluate the feasibility, repeatability, and efficiency of our self-calibration method. The experimental data shows that the average absolute error of the Euler angles and translation vectors obtained by our method relative to the reference values obtained by Zhang’s calibration method does not exceed 0.5˚ and 2 mm, respectively. The distribution range of the most widely spread parameter in Euler angles is less than 0.2˚ while that in translation vectors does not exceed 2.15 mm. Under the general texture scene and the normal driving speed of the mobile robot, the calibration time can be generally maintained within 10 s. The above results prove that our proposed method is reliable and has practical value.