Map construction in large scale outdoor environment is of importance for robots to robustly fulfill their tasks. Massive sessions of data should be merged to distinguish low dynamics in the map, which otherwise might ...
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In this paper,based on Inertial Measurement Unit（IMU） preintegration,a pose estimation algorithm for Visual-Inertial Odometry（VIO） system has been ***,we developed the kinematic equation of acceleration and angula...
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In this paper,based on Inertial Measurement Unit（IMU） preintegration,a pose estimation algorithm for Visual-Inertial Odometry（VIO） system has been ***,we developed the kinematic equation of acceleration and angular velocity of *** avoid the re-integration caused by the change of the system states,the IMU measurements are preintegrated on the Riemannian manifold of ***,we studied the propagation characteristic of noise and derived the state transition equation of the *** the influence of noise on pose estimation can be dramatically *** with the famous Okvis algorithm,experimental results on two public datasets of EuRoC show that the proposed IMU preintegration algorithm achieves good estimation median of translation error with 0.14 m,which is better than Okvis with 0.24 m,
The emerging development of connected and automated vehicles imposes a significant challenge on current vehicle control and transportation systems. This paper proposes a novel unified approach, Parallel Driving, a clo...
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The emerging development of connected and automated vehicles imposes a significant challenge on current vehicle control and transportation systems. This paper proposes a novel unified approach, Parallel Driving, a cloud-based cyberphysical-social systems(CPSS) framework aiming at synergizing connected automated driving. This study first introduces the CPSS and ACP-based intelligent machine systems. Then the parallel driving is proposed in the cyber-physical-social space,considering interactions among vehicles, human drivers, and information. Within the framework, parallel testing, parallel learning and parallel reinforcement learning are developed and concisely reviewed. Development on intelligent horizon(iHorizon)and its applications are also presented towards parallel *** proposed parallel driving offers an ample solution for achieving a smooth, safe and efficient cooperation among connected automated vehicles with different levels of automation in future road transportation systems.
In this paper, we consider the `q−regularized kernel regression with 0 q−penalty term over a linear span of features generated by a kernel function. We study the asymptotic behavior of the algorithm under the framewor...
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The vision-based target recognition and tracking have received much attention in the field of robotics. Existing methods mainly focus on the vision perception of individual robot with a single view, however, the perfo...
The vision-based target recognition and tracking have received much attention in the field of robotics. Existing methods mainly focus on the vision perception of individual robot with a single view, however, the performance is susceptible to illumination and occlusion. Multi-robot collaborative perception provides a potential solution to deal with the limitation of single-view observation, however, the challenging of environmental adaptability for multi-robot collaborative decision still remains unsolved. To solve this problem, this paper proposes a two-level adaptive target recognition and tracking method based on vision for multi-robot system. The problem of multi-robot target recognition and tracking is solved under a two-level framework, which contains the features fusion level of individual robot and the cooperation level of multi-robot system. In the first level, the features measuring results that influence the visual perception of individual robot are fused, while the second level combines the voting of each robot together to determine the target for multi-robot system. Both the features measuring weights and robots voting weights are adaptively updated according to their evaluation, which lead to a beneficial result where the features and robots with higher accuracy play major roles in the first and second levels, respectively. Therefore, a good adaptability to the environments can be guaranteed. The experimental results show that the proposed approach can realize the coordination of multi-robot system in target recognition and tracking with an effective performance.
Background: systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, ...
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Network is the core element of many industrial and automation systems. In the future, more attention will be paid to convergent and unified network. Time-critical traffic and non-time-critical traffic need to share co...
Network is the core element of many industrial and automation systems. In the future, more attention will be paid to convergent and unified network. Time-critical traffic and non-time-critical traffic need to share communication channels. The goal of the IEEE TSN task group is to extend the existing Ethernet standard to achieve a degree of certainty that meets the hard-real-time requirements of modern control networks in the industrial automation and automotive industries. Time sensitive network (TSN) is characterized by low jitter, low delay and deterministic transmission. It can transmit critical and non-critical traffic in the same network, which is very suitable for time- critical applications with high requirements on transmission delay. This paper briefly summarizes the key components of TSN, studies the key technologies, and carries out a simple traffic scheduling experiment on the switch based on 802.1Qbv function, and finally analyzes the application of TSN in industrial automation and automobile industry.
Recognize an object and detect a good grasp in unstructured scenes is still a challenge. In this paper, the problem of detecting robotic grasps is expressed by a two-point representation in an unstructured scene with ...
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ISBN:
(纸本)9781728103785;9781728103778
Recognize an object and detect a good grasp in unstructured scenes is still a challenge. In this paper, the problem of detecting robotic grasps is expressed by a two-point representation in an unstructured scene with an RGB-D camera. A deep Convolutional Neural Network is designed to predict good grasps in real-time on GTX1080, with using region proposal techniques. A contribution of this work is our proposed network framework can perform classification, location and grasp detection simultaneously so that in a single step, it not only recognizes the category and bounding-box of the object, but also finds a good grasp line. Besides, in training process, we minimize a multi-task loss objective function of object classification, location and grasp detection in order to train the network end-to-end. Our experimental evaluation on a real robotic manipulator demonstrates that the robotic manipulator can fulfill the grasping task effectively.
In this paper,we study a class of stochastic fuzzy delayed Cohen-Grossberg neural networks under impulsive *** Razumikhin method and iteration technique,some sufficient conditions ensuring the mean-square exponential ...
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In this paper,we study a class of stochastic fuzzy delayed Cohen-Grossberg neural networks under impulsive *** Razumikhin method and iteration technique,some sufficient conditions ensuring the mean-square exponential input-to-state stability of the designed delayed neural networks are *** results can generalize and improve some earlier publications.A numerical example is demonstrated to verify the theoretical results.
LIDAR and odometer are used as the main sensors in this paper, with the two-wheeled self-balancing robot as the research and experiment platform, this topic has researched the way to SLAM when the robot is in unfamili...
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ISBN:
(纸本)9781509046584
LIDAR and odometer are used as the main sensors in this paper, with the two-wheeled self-balancing robot as the research and experiment platform, this topic has researched the way to SLAM when the robot is in unfamiliar environment with uncertain position and orientation. On the issue of setting the dynamic threshold value to zone the LIDAR scanning points in the process of constructing the geometry map, with the consideration of the character of RPLIDAR which is used in our research, this paper has analyzed and proposed the specific dynamic threshold values, which make the zoning of LIDAR scanning points more reasonable. In order to improve the robustness of SLAM, we have used regular particle filter(RPF) as the location algorithm. In order to solve the problem of unable to add auxiliary information under the traditional MCL framework, we have taken full use of the high accuracy character of the geometry matching and locating, and have used the result of it to improve the importance density function of RPF. Based on the idea of Rao-Blackwellization, the improved RBPF-SLAM has been proposed. The validity and feasibility of the improved RBPF-SLAM have been proved with simulations and experiments.
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