Current simultaneous localization and mapping (SLAM) algorithms perform well in static environments but easily fail in dynamic environments. Recent works introduce deep learning-based semantic information to SLAM systems to reduce the influence of dynamic objects. However, it is still challenging to apply a robust localization in dynamic environments for resource-restricted robots. This paper proposes a real-time RGB-D inertial odometry system for resource-restricted robots in dynamic environments named Dynamic-VINS. Three main threads run in parallel: object detection, feature tracking, and state optimization. The proposed Dynamic-VINS combines object detection and depth information for dynamic feature recognition and achieves performance comparable to semantic segmentation. Dynamic-VINS adopts grid-based feature detection and proposes a fast and efficient method to extract high-quality FAST feature points. IMU is applied to predict motion for feature tracking and moving consistency check. The proposed method is evaluated on both public datasets and real-world applications and shows competitive localization accuracy and robustness in dynamic environments. Yet, to the best of our knowledge, it is the best-performance real-time RGB-D inertial odometry for resource-restricted platforms in dynamic environments for now. The proposed system is open source at: https://github.com/HITSZ-NRSL/Dynamic-VINS.git

Actively planning sensor views during object reconstruction is crucial for autonomous mobile robots. An effective method should be able to strike a balance between accuracy and efficiency. In this paper, we propose a seamless integration of the emerging implicit representation with the active reconstruction task. We build an implicit occupancy field as our geometry proxy. While training, the prior object bounding box is utilized as auxiliary information to generate clean and detailed reconstructions. To evaluate view uncertainty, we employ a sampling-based approach that directly extracts entropy from the reconstructed occupancy probability field as our measure of view information gain. This eliminates the need for additional uncertainty maps or learning. Unlike previous methods that compare view uncertainty within a finite set of candidates, we aim to find the next-best-view (NBV) on a continuous manifold. Leveraging the differentiability of the implicit representation, the NBV can be optimized directly by maximizing the view uncertainty using gradient descent. It significantly enhances the method's adaptability to different scenarios. Simulation and real-world experiments demonstrate that our approach effectively improves reconstruction accuracy and efficiency of view planning in active reconstruction tasks. The proposed system is open-sourced at https://github.com/HITSZ-NRSL/ActiveImplicitRecon.git .

This paper presents an efficient approach to achieve microparticles flocking with robotics and optical tweezers technologies. All particles trapped by optical tweezers can be automatically moved toward a predefined region without collision. The main contribution of this paper lies in the proposal of several solutions to the flocking manipulation of microparticles in microenvironments. First, a simple flocking controller is proposed to generate the desired positions and velocities for particles' movement. Second, a velocity saturation method is implemented to prevent the desired velocities from exceeding a safe limit. Third, a two-layer control architecture is proposed for the motion control of optical tweezers. This architecture can help make many robotic manipulations achievable under microenvironments. The proposed approach with these solutions can be applied to many bioapplications especially in cell engineering and biomedicine. Experiments on yeast cells with a robot-tweezers system are finally performed to verify the effectiveness of the proposed approach.

Transportation of biological cells has attracted increased attention in bioscience and nanomedicine. Existing approaches to achieve automated multicell transportation are generally based on numerous overstrict conditions or assumptions, including static and clean environments, complex theoretical convergence conditions, omitting tool kinematics, and off-line calibrations. This paper presents a novel approach for the automated transportation of multiple cells by using robotically controlled holographic optical tweezers. First, a swarming controller is developed with easily satisfied convergence conditions. The offset between centers of the cell and optical tweezers is constrained by saturation control to maintain the cells in the optically trapping area. An artificial first-order kinematic model of the tweezers is considered in the controller design to reduce controller oscillation. Second, a solution to the collision avoidance of random-moving obstacles is developed to remove the assumption of static or clean environments. Finally, an automated method based on the drag force model and gradient descent optimization is presented to calibrate cell dynamics online. Experiments on yeast cells are performed to verify the effectiveness of the proposed approach.

Autonomous grasping with an aerial manipulator in the applications of aerial transportation and manipulation is still a challenging problem because of the complex kinematics/dynamics and motion constraints of the coupled rotors-manipulator system. The paper develops a novel aerial manipulation system with a lightweight manipulator, an X8 coaxial octocopter and onboard visual tracking system. To implement autonomous grasping control, we develop a novel and efficient approach that includes trajectory planning, visual trajectory tracking and kinematic compensation. Trajectory planning for aerial grasping control is formulated as a multi-objective optimization problem, while motion constraints and collision avoidance are considered in the optimization. A genetic method is applied to obtain the optimal solution. A kinematic compensation-based visual trajectory tracking is introduced to address the coupled affection between the manipulator and octocopter, with the advantage of discarding the complex dynamic parameter calibration. Finally, several experiments are performed to verify the effectiveness of the proposed approach.

State estimation in complex illumination environments based on conventional visual-inertial odometry is a challenging task due to the severe visual degradation of the visual camera. The thermal infrared camera is capable of all-day time and is less affected by illumination variation. However, most existing visual data association algorithms are incompatible because the thermal infrared data contains large noise and low contrast. Motivated by the phenomenon that thermal radiation varies most significantly at the edges of objects, the study proposes an ETIO, the first edge-based monocular thermal-inertial odometry, for robust state estimation in visually degraded environments. Instead of the raw image, we utilize the binarized image from edge extraction for pose estimation to overcome the poor thermal infrared image quality. Then, an adaptive feature tracking strategy ADT-KLT is developed for robust data association based on limited edge information and its distance distribution. Finally, a pose graph optimization performs real-time estimation over a sliding window of recent states by combining IMU pre-integration with reprojection error of all edge feature observations. We evaluated the performance of the proposed system on public datasets and real-world experiments and compared it against state-of-the-art methods. The proposed ETIO was verified with the ability to enable accurate and robust state estimation all-day time.