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5th International Conference on Robotics, Control and Automation engineering, RCAE 2022

作者： Qiu, Xuewen Liang, Huawei Wang, Jie Jing, Junsen Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei China University of Science and Technology of China Hefei China Institutes of Physical Science and Information Technology Anhui University Hefei China Anhui Engineering Laboratory for Intelligent Driving Technology and Application China Chinese Academy of Sciences China

ISBN: (纸本)9781665459419

The vehicle-mounted cameras provide video streaming information for the post-processing phase of the intelligent vehicle, such as control and decision making. According to the characteristics of in-vehicle videos, this paper studies a video stabilization algorithm considering different depths of field. On the motion estimation stage, for the problem of inaccurate estimation inter-frame motion vectors caused by different depths of field of feature points, this paper proposes a method to repair inter-frame motion vectors. The method can correct the feature point position of the current frame and improve the estimation accuracy of the global motion vector. In the motion smoothing phase, a real-time online optimization framework and adaptive weights are used to balance the relationship between motion smoothing and motion following. Experiments prove that the accuracy of motion estimation of the algorithm in this paper is better than other improved algorithm, and the average value of PSNR after video stabilization is increased by more than 3dB compared with the original videos. © 2022 IEEE.

关键词： Motion estimation

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A real-time vehicle detection algorithm based on sparse point clouds and dempster-shafer fusion theory

A real-time vehicle detection algorithm based on sparse poin...

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2018 IEEE International Conference on Information and Automation, ICIA 2018

作者： Xu, Fengyu Liang, Huawei Wang, Zhiling Lin, Linglong Chu, Zhiwei Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei China University of Science and Technology of China Hefei China Anhui Engineering Laboratory for Intelligent Driving Technology and Appllication Hefei China Anhui Province Key Laboratory of Intelligent Connected Technology Hefei China Northeastern University Shenyang China

ISBN: (纸本)9781538680698

In order to improve real-time performance and reduce the dependence on computing resources, we present a novel vehicle detection algorithm based on sparse point clouds in this paper. In point clouds segmentation, virtual laser line is proposed and our fast two-step segmentation method has proved to be time-efficient. Since accuracy and real-time capabilities are all crucial for autonomous vehicles, we utilize multiple features analysis and dempster-shafer(D-S) fusion theory to improve detection accuracy. Validation tests and experimental results show our method has a high performance in real urban traffic situations. © 2018 IEEE.

关键词： Vehicles

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Multiscale Space Adaptation-Based Dynamic Object Removal for LiDAR Mapping

Multiscale Space Adaptation-Based Dynamic Object Removal for...

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2023 IEEE International Conference on Unmanned Systems, ICUS 2023

作者： Xu, Haitao Wang, Hanqi Zhang, Xueshi Zhou, Pengfei Liang, Huawei Institutes of Physical Science and Information Technology Anhui University Hefei230031 China Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei230031 China Anhui Engineering Laboratory for Intelligent Driving Technology and Application Hefei230031 China Innovation Research Institute of Robotics and Intelligent Manufacturing Chinese Academy of Sciences Hefei230031 China

ISBN: (纸本)9798350316308

Simultaneous Localization And Mapping (SLAM) is a crucial technology for mobile robots to locate themselves when working in a location environment. However, when a large number of dynamic objects appear in the environment, the positioning and mapping results of the robot will be affected. At present, online real-time removal algorithms can effectively solve such problems. Therefore, this paper proposes a dynamic object removal algorithm based on multi-scale spatial information, which identifies and eliminates moving objects in the environment during self-localization and environmental modeling. In this paper, the Inertial Measurement Unit (IMU) data is used to pre-liminarily judge the robot's position and attitude transformation, and the dynamic target distance is combined to adjust the target feature extraction scale, and modify the dynamic object detection conditions to screen objects with high confidence. To remove misjudgment information caused by irregular objects in the environment, road information is used to remove misjudgment information and obtain dynamic object coordinate information in the environment. Through quantitative and qualitative experiments, it has been proven that this algorithm can improve the positioning accuracy of robots in urban road scenes and optimize environmental models. This algorithm reduces Absolute Pose Error by 0.11032m. © 2023 IEEE.

关键词： Mapping

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An Improved Feature-Based Visual Slam Using Semantic Information 6

An Improved Feature-Based Visual Slam Using Semantic Informa...

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6th IEEE Information technology, Networking, Electronic and Automation Control Conference, ITNEC 2023

作者： Ma, Songyu Liang, Huawei Wang, Hanqi Xu, Tiejuan Anhui Jianzhu University Hefei230009 China Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei230031 China Innovation Research Institute of Robotics and Intelligent Manufacturing Chinese Academy of Sciences Hefei230088 China Anhui Engineering Laboratory for Intelligent Driving Technology and Application Hefei230088 China University of Science and Technology of China Hefei230026 China

ISBN: (纸本)9781665460033

The traditional visual slam method for feature extraction is single and has poor robustness. This paper proposes an improved feature-based SLAM (Simultaneous localization and mapping) by adding weights to the features of objects matching the same semantic category and incorporating semantic information into loop closure detection. The basic idea is to use the deep neural network YOLOV5 to classify things, associate feature points with objects appearing in the bounding box, and thus assign the feature points to the semantic labels of these objects. In the feature matching process of the SLAM algorithm, the matching of feature points with the same semantic label will be weight, which increases the matching of similar features on the same category of objects, and at the same time intefrates semantic information into loop detection to improve the accuracy of loop closure detection. The test results show that the absolute trajectory error of the improved algorithm is more minor, the pose estimation accuracy is higher, and the tracking robustness can be effectively improved. © 2023 IEEE.

关键词： Feature extraction

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Motion State Estimation Based on Multi-sensor Fusion and Noise Covariance Estimation

Motion State Estimation Based on Multi-sensor Fusion and Noi...

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International Conference on Autonomous Unmanned Systems, ICAUS 2021

作者： Jiang, Chao Wang, Zhiling Liang, Huawei Zhang, Shijing Tan, Shuhang Hefei Institutes of Physical Science Chinese Academy of Sciences HeFei230031 China University of Science and Technology of China HeFei230026 China Anhui Engineering Laboratory for Intelligent Driving Technology and Application HeFei230061 China Innovation Research Institute of Robotics and Intelligent Manufacturing Chinese Academy of Sciences HeFei230061 China

ISBN: (纸本)9789811694912

Detecting or estimating the state of the moving target accurately on the road helps predict the behavior of the moving target, and this is beneficial for self-vehicles’ trajectory planning and safe navigation. The difficulty of state estimation lies in how to estimate the state of the target in real-time and accurately, and it will not be divergence when the sensor has missed detection, false detection and time-varying measurement noise. In response to this difficulty, it is proposed to use multi-source information fusion and Kalman filter to make up for the missed detection of the sensor, use the nearest neighbor correlation algorithm to match the detection and prediction to reduce the impact of false detection, and use the adaptive measurement noise estimation method to estimate the current value of the measurement noise in real-time to improve the stability and accuracy of the filter when the measurement noise is time-varying. The proposed method relies on the smart car platform for experiments. The experimental results show that the position and velocity estimation accuracy of the proposed algorithm are all better than the comparison method. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Spurious signal noise

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Cooperative Route Planning for Fuel-constrained UGV-UAV Exploration

Cooperative Route Planning for Fuel-constrained UGV-UAV Expl...

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2022 IEEE International Conference on Unmanned Systems, ICUS 2022

作者： Zhang, Mingjia Liang, Huawei Zhou, PengFei Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei China University of Science and Technology of China Hefei China Nanjing Polytechnic Institute Nanjing China Anhui Engineering Laboratory for Intelligent Driving Technology and Application Hefei China Innovation Research Institute of Robotics and Intelligent Manufacturing Chinese Academy of Sciences Hefei China

ISBN: (数字)9781665484565

ISBN: (纸本)9781665484565

Heterogeneous UGV-UAV system can efficiently complete large-scale tasks such as exploration and rescue by collaborative operation. This paper proposes a route planner based on a dynamic aggregation strategy to select the time-optimal routes for function-constrained UGV and fuel-constrained UAV to reach all the mission points. To reduce the time consumption of waiting at the take-off point, UGV and UAV are inclined to rendezvous at the next meeting point by no-wait operations. We present a two-step routing strategy to address this cooperative problem. First, the Voronoi diagram and greedy set cover method are used to determine meeting points and task areas. Then, solving a traveling salesman problem to find a UGV path by A-star and Lin-Kernighan heuristic. After that, an adaptive large neighborhood search heuristic is proposed to address vehicle routing problem with recharge for UAV among multiple task areas. The simulation and real experiment results demonstrate that our approach can effectively generate a time-optimal path for UGV-UAV Cooperative exploration. © 2022 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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UGV-UAV Cooperative 3D Multi-Object Tracking Based on Multi-Source Data Fusion

UGV-UAV Cooperative 3D Multi-Object Tracking Based on Multi-...

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2023 IEEE International Conference on Unmanned Systems, ICUS 2023

作者： Zhang, Mingjia Liang, Huawei Zhou, Pengfei Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei China University of Science and Technology of China Hefei China Nanjing Polytechnic Institute Nanjing China Anhui Engineering Laboratory for Intelligent Driving Technology and Application Hefei China Innovation Research Institute of Robotics and Intelligent Manufacturing Chinese Academy of Sciences Hefei China

ISBN: (纸本)9798350316308

Unmanned aerial vehicles (UAVs) and Unmanned ground vehicles (UGV s) have complementary sensing characteristics, and their cooperative sensing can effectively solve the problems of target loss due to view occlusion or distance in multi-target tracking tasks. In this paper, we propose a track by detection framework based on a multi-source data fusion approach. In the detection phase, a modified ImvoxelN et network with attention mechanism is proposed to fuse the multi-view image data of unmanned vehicles and UAVs to estimate the position and size of the target. In the tracking stage, the method involves correlation processing of both 2D and 3D detection data. It utilizes an interactive multi-mode unscented Kalman filter (IMM-UKF) algorithm that incorporates the Minimum-update Successive Shortest Path (muSSP) method to enhance the accuracy and real-time performance of dynamic target tracking. The experimental results demonstrate the effectiveness of the proposed approach in significantly improving the performance of multi-target tracking tasks. © 2023 IEEE.

关键词： Data fusion

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DNet-CNet: a novel cascaded deep network for real-time lane detection and classification

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Journal of Ambient Intelligence and Humanized Computing 2023年第8期14卷 10745-10760页

作者： Zhang, Lu Jiang, Fengling Yang, Jing Kong, Bin Hussain, Amir Gogate, Mandar Dashtipour, Kia Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei230031 China University of Science and Technology of China Hefei230026 China Anhui Engineering Laboratory for Intelligent Driving Technology and Application Hefei230088 China Hefei Normal University Hefei230601 China Hefei University Hefei230601 China School of Computing Edinburgh Napier University EdinburghEH10 5DT United Kingdom

Robust understanding of the lane position and type is essential for changing lanes in autonomous vehicles. However, accomplishing this task in real time with high level of precision is not trivial. In this paper, we propose a novel cascaded deep neural network (DNet-CNet) for real-time end-to-end lane detection (DNet) and classification (CNet). The proposed model can simultaneously predict the lanes position and types. DNet integrates the spatial features extracted from the encoder with those from the decoder to compensate for the lower dimensional encoded data and edge information. Furthermore, the output of DNet is fused with the input image for real time lightweight lane classification model (CNet). The combined features exploit the inherent colors and shape of lanes to improve classification accuracy. Experimental results on the benchmark TuSimple, Caltech-lanes and ELAS datasets show that, the model proposed achieves superior lane detection and classification accuracy in real-time as compared to Cascade-CNN. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Classification (of information)

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Closed-Loop Simulation Test Method for Autonomous Vehicles Based on Adversarial Scenarios 20

Closed-Loop Simulation Test Method for Autonomous Vehicles B...

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20th IEEE International Conference on Mechatronics and Automation, ICMA 2023

作者： Zheng, Xiaokun Liang, Huawei Li, Zhiyuan Hua, Chen Wu, Qiong Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei230031 China University of Science and Technology of China Hefei230026 China Anhui Engineering Laboratory for Intelligent Driving Technology and Appllication Anhui Hefei230008 China Anhui Jianghuai Automobile Group Co. Ltd Anhui Hefei230008 China

ISBN: (纸本)9798350320831

Using simulation technology to verify the safety of autonomous vehicles (AVs) is a critical aspect of the development of autonomous driving technology. The primary objective is to reduce the time and cost of AV testing while enhancing testing efficiency. In this study, we propose a closed-loop simulation test method for AVs based on an adversarial scenario generation process. A comprehensive closed-loop simulation system was established, incorporating Simulink, Prescan, Carsim, and hardware-in-the-loop *** swiftly identify challenging test scenarios, we formulated an optimization model for safety evaluation, which is based on an improved driving safety field model. We employed the genetic algorithm to effectively solve this optimization problem, allowing for the rapid generation of difficult *** results demonstrate that our proposed method can effectively generate challenging scenarios, enabling the swift confirmation of the safety performance boundaries of the system under test. This approach significantly improves the overall testing efficiency and provides valuable insights for the advancement of autonomous driving technologies. © 2023 IEEE.

关键词： Genetic algorithms

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A New Free-Standing Aqueous Zinc-Ion Capacitor Based on MnO2–CNTs Cathode and MXene Anode

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Nano-Micro Letters 2019年第4期11卷 227-238页

作者： Siliang Wang Qiang Wang Wei Zeng Min Wang Limin Ruan Yanan Ma Key Laboratory of Intelligent Computing and Signal Processing Ministry of EducationAnhui UniversityNo.3 Feixi RoadHefei 230039Anhui ProvincePeople’s Republic of China National Engineering Research Center for Agro-Ecological Big Data Analysis and Application School of Electronics and Information EngineeringAnhui UniversityNo.111 Jiulong RoadHefei 230601Anhui ProvincePeople’s Republic of China School of Sciences Hubei University of Automotive TechnologyNo.167 Checheng West RoadShiyan 442002Hubei ProvincePeople’s Republic of China

Restricted by their energy storage mechanism,current energy storage devices have certain drawbacks,such as low power density for batteries and low energy density for ***,the nearest ion capacitors,such as lithium-ion and sodium-ion capacitors containing battery-type and capacitor-type electrodes,may allow achieving both high energy and power *** the inspiration,a new zinc-ion capacitor(ZIC)has been designed and realized by assembling the free-standing manganese dioxide-carbon nanotubes(MnO2-CNTs)battery-type cathode and MXene(Ti3C2Tx)capacitortype anode in an aqueous *** ZIC can avoid the insecurity issues that frequently occurred in lithium-ion and sodium-ion capacitors in organic *** expected,the ZIC in an aqueous liquid electrolyte exhibits excellent electrochemical performance(based on the total weight of cathode and anode),such as a high specific capacitance of 115.1 F g?1(1 mV s?1),high energy density of 98.6 Wh kg?1(77.5 W kg?1),high power density of 2480.6 W kg?1(29.7 Wh kg?1),and high capacitance retention of^83.6%of its initial capacitance(15,000 cycles).Even in an aqueous gel electrolyte,the ZIC also exhibits excellent *** work provides an essential strategy for designing next-generation high-performance energy storage devices.

关键词： Energy storage Zinc-ion capacitor Battery-type and capacitor-type electrodes MXene Electrochemical performance

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