Keyframe selection plays a crucial role in balancing computational efficiency and localization accuracy in Visual Simultaneous Localization and Mapping (VSLAM) systems. Existing keyframe selection methods often struggle to capture high-level semantic information in environments where multiple semantic dimensions interact. In this paper, we propose the Multi-dimensional Semantic Analysis (MSA) module based on Visual-Language Models (VLMs). By leveraging the capability of VLMs to extract rich semantic features, we compute the similarity between each image frame and a set of textual descriptions, generating a scene descriptor that quantifies the semantic distance between frames across multiple dimensions (e.g., object count, texture, and lighting). We then introduce the Scene Change Assessment (SCA) module based on Bayesian On-line Changepoint Detection (BOCD), which identifies keyframes with significant semantic information gain, thereby reducing the total number of keyframes. Extensive experiments on an open dataset demonstrate that our method not only significantly reduces the number of keyframes but also maintains high localization accuracy. Furthermore, the inference speed of the MSA module satisfies the real-time requirements of VSLAM. These results underscore the potential of our approach to enhance the efficiency of keyframe selection.

Limited by the working principles, LiDAR-SLAM systems suffer from the degeneration phenomenon in environments such as long corridors and tunnels, due to the lack of sufficient geometric features for frame-to-frame matching. The accuracy and sensitivity of existing degeneracy detection methods need to be further improved. In this paper, we propose a novel method for degeneracy detection using local geometric models based on point-to-distribution matching. To obtain an accurate description of local geometric models, an adaptive adjustment of voxel segmentation according to the point cloud distribution and density is designed. The codes of the proposed method is open-source and available at https://github.com/jisehua/Degenerate-Detection.git. Experiments with public datasets and self-build robots were conducted to evaluate the methods. The results exhibit that our proposed method achieves higher accuracy than the other existing approaches. Applying our proposed method is beneficial for improving the robustness of the LiDAR-SLAM systems.

As a hard high-altitude work, power transmission line inspection increasingly demands robots to conduct in place of the human being. A variety of robots have been developed to this end, with basic locomotion and inspection implemented on the lines. However, most current line inspection robots (LIRs) are only mobile platforms with complex structures and large weights, lacking sufficiently dexterous locomotion on lines, especially for obstacle overcoming and line transition. Also with sensors fixed on the platform, the inspection range is largely limited. For higher mobility and a larger inspection range, a novel biped robot that can roll and climb on a power transmission line for inspection, called Climbot-L, is proposed in this paper. While the rolling mode has the advantage of high locomotion efficiency, the biped climbing mode makes it possible to easily overcome obstacles on the line, transition to adjacent cables, and have multi-view detection. In this paper, the design of this novel robot is first introduced, the working principle of the wheel-gripper modules is then analyzed, and obstacle overcoming gaits are stated. The effectiveness and high maneuverability of the presented robot are verified by a series of experiments.

Spatio-temporal information is key to resolve occlusion and depth ambiguity in 3D human pose estimation. Previous methods have focused on either temporal contexts or local-to-global architectures that embed fixed-length spatiotemporal information. To date, there have not been effective proposals to simultaneously and flexibly capture varying spatiotemporal sequences and effectively achieves real-time 3D human pose estimation. In this work, we improve the learning of kinematic constraints in the human skeleton: posture, local kinematic connections, and symmetry by modeling local and global spatial information via attention mechanisms. To adapt to single- and multi-frame estimation, the dilated temporal model is employed to process varying skeleton sequences. Also, importantly, we carefully design the interleaving of spatial semantics with temporal dependencies to achieve a synergistic effect. To this end, we propose a simple yet effective graph attention spatio-temporal convolutional network (GAST-Net) that comprises of interleaved temporal convolutional and graph attention blocks. Experiments on two challenging benchmark datasets (Human3.6M and HumanEva-I) and YouTube videos demonstrate that our approach effectively mitigates depth ambiguity and self-occlusion, generalizes to half upper body estimation, and achieves competitive performance on 2D-to-3D video pose estimation. Code, video, and supplementary information is available at: http://www.juanrojas.net/gast/

Soft climbing robots have been attracting increasing attention in soft robotics community, and a lot of prototypes been proposed with basic climbing function implemented. Climbing on poles is a challenge with soft robots, and the capability of current pole-climbing soft robots needs to be improved in terms of adaptability to various poles and deformation controllability or constraining of the soft body. In this paper, a rigid-compliant coupling or constraint method is proposed in design of pole climbing robots. Specifically, a novel gripper with inner expanding bubbles and a rigid-compliant belt is presented and designed, featured with excellent shape/size-adaptability and grip-reliability. To constrain undesired deformation of the soft body, rigid-compliant belts are also adopted. Three rigid-compliant constrained actuators are connected to implement an Ω-shaped deformation of the body for climbing motion. The kinematic feature of the body is established, and the two main features of the inner expanding gripper are analyzed. A series of experiments, where the robot climbs on poles with different shapes and sizes and in different poses, have verified the effectiveness of the presented rigid-compliant constraint and the shape/size adaptability and grip-reliability of the novel inner expanding grippers.

Climbing or crawling robots may be widely applied in agriculture, forestry, military, construction industry, disaster searching and rescuing, and so on. Soft robots possess better safety, flexibility, dexterity, portability, and adaption to complex environments than traditional robots. However, there are big challenges in system development, modeling and control of soft climbing robots. To address system development of a soft robot as a new type climbing robot, we present a pneumatic soft robot capable of inchworm-like locomotion, PISRob. The presented robot is composed of three soft parts in H-shaped configuration. Each part is able to perform 2D bending. While the middle part, as the main body, can bend in Ω -shape for actuation, the two end parts as legs can conduct simple bending motion for grasping or anchoring during locomotion. The system design and fabrication process of the soft robot is presented in details in this paper. A control system is developed for pneumatic actuation of the robot. Tests are carried out to get the relationship between the actuating air pressure and the step length in locomotion. Experiments of crawling on a floor and climbing on a pole are performed to verify the feasibility of development of the new soft robot and the effectiveness of the control method for the pneumatic system.

Automating item picking in the e-commerce warehouse is pressing but challenging, due to a massive variety of items, tight environmental constraints and item location uncertainty. In this paper, we present an effective and efficient strategy-based planning approach to implement the robotic picking from shelves for e-commerce. Making full advantage of a gripper with multiple securing methods, differentiated strategies are modeled as picking primitives with different securing methods. A strategy generator is proposed to produce feasible potential pickings as quickly and as successfully as possible. A strategy evaluator considering reachability, collision, object-bias preference and the securing performance is also presented for ranking the picking strategies. Experiments were conducted to validate that the robotic picker is able to plan a picking strategy within 2 ms and pick daily items from the shelves with an average success rate of 68%.

Combining the advantages of active and passive walking robots, we have developed a novel active biped walking robot with only six DoFs. The robot is built with six 1-DoF joint modules and two wheels as the feet. It achieves locomotion in special gaits different from those of traditional biped robots. In this paper, this novel biped robot is introduced, and four walking gaits, namely turning-around gait, foot-wheel hybrid gait, side-stepping gait and turning-over gait are proposed, and their walking patterns and motion planning are presented and analyzed. Walking experiments are carried out to verify the locomotion function, the effectiveness of the presented gaits and to illustrate the features of this novel biped robot. It has been shown that biped active walking may be achieved with only a few DoFs and simple kinematic configuration.

High-rise tasks in agriculture, forestry and building industry requires robots possessing climbing function. Motivated by these potential applications and inspired by the climbing motion of animals such as inchworms, we have developed a novel biped climbing robot - Climbot. Built with a modular approach, the robot consists of five 1-DoF joint modules connected in series and two special grippers mounted at the ends. With this configuration, Climbot is able not only to climb a variety of media, but also to grasp and manipulate objects, and hence is a ¿mobile¿ manipulator. In this paper, we first introduce the development of this novel robot, and then illustrate three climbing gaits based on the unique configuration of the robot. Experiments of climbing poles are carried out to verify the climbing functions and to demonstrate potential application of the proposed robot.