Ling Chen

Bio: Ling Chen is an academic researcher from Hunan Normal University. The author has contributed to research in topics: Extended Kalman filter & Simultaneous localization and mapping. The author has an hindex of 11, co-authored 28 publications receiving 262 citations. Previous affiliations of Ling Chen include University of Essex & Shanghai University.

Towards autonomous localization and mapping of AUVs: a survey

Abstract: Purpose – The main purpose of this paper is to investigate two key elements of localization and mapping of Autonomous Underwater Vehicle (AUV), i.e. to overview various sensors and algorithms used for underwater localization and mapping, and to make suggestions for future research.Design/methodology/approach – The authors first review various sensors and algorithms used for AUVs in the terms of basic working principle, characters, their advantages and disadvantages. The statistical analysis is carried out by studying 35 AUV platforms according to the application circumstances of sensors and algorithms.Findings – As real‐world applications have different requirements and specifications, it is necessary to select the most appropriate one by balancing various factors such as accuracy, cost, size, etc. Although highly accurate localization and mapping in an underwater environment is very difficult, more and more accurate and robust navigation solutions will be achieved with the development of both sensors and...

Doorway passing of an intelligent wheelchair by dynamically generating Bézier curve trajectory

Abstract: Door passing is the basic capability of an intelligent wheelchair. This paper presents a novel strategy to address the door passing issue by dynamically generating the Be´zier curve based trajectory. It consists of door finding, optimization based trajectory generation and tracking control, which are executed repeatedly to increase the ability of passing the door and improve the performance. Whenever the door is detected, the optimization method produces a new smooth reference trajectory in real time for the wheelchair to follow. The proposed approach is tested in reality to verify its feasibility and efficiency, and the experimental results show its good performance in terms of the accuracy of finding the door and passing the doorway.

IMU/GPS based pedestrian localization

Abstract: The low cost Inertial Measurement Unit(IMU) can be used to provide accurate position information of a pedestrian when it is combined with Global Positioning System(GPS). This paper investigates how the integration of IMU anf GPS can be effectively used in pedestrian localization. The position calculation is achieved in sequence by three different strategies, namely basic double integration of IMU data, Zero-velocity Update (ZUPT) and Extended Kalman Filter(EKF) based fusion of IMU and GPS data. Experiments that are conducted in two fields show that EKF based localization outperform the double integration and ZUPT methods in terms of both positioning accuracy and robustness.

GraspCNN: Real-Time Grasp Detection Using a New Oriented Diameter Circle Representation

Abstract: This paper proposes GraspCNN, an approach to grasp detection where a feasible robotic grasp is detected as an oriented diameter circle in RGB image, using a single convolutional neural network. By detecting robotic grasps as oriented diameter circles, grasp representation is thereby simplified. In addition to our novel grasp representation, a grasp pose localization algorithm is proposed to project an oriented diameter circle back to a 6D grasp pose in point cloud. GraspCNN predicts feasible grasping circles and grasp probabilities directly from RGB image. Experiments show that GraspCNN achieves a 96.5% accuracy on the Cornell Grasping Dataset, outperforming existing one-stage detectors for grasp detection. GraspCNN is fast and stable, which can process RGB image at 50 fps and meet the requirements of real-time applications. To detect objects and locate feasible grasps simultaneously, GraspCNN is executed in parallel with YOLO, which achieves outstanding performance on both object detection and grasp detection.

SeqSLAM : visual route-based navigation for sunny summer days and stormy winter nights

Abstract: Learning and then recognizing a route, whether travelled during the day or at night, in clear or inclement weather, and in summer or winter is a challenging task for state of the art algorithms in computer vision and robotics. In this paper, we present a new approach to visual navigation under changing conditions dubbed SeqSLAM. Instead of calculating the single location most likely given a current image, our approach calculates the best candidate matching location within every local navigation sequence. Localization is then achieved by recognizing coherent sequences of these “local best matches”. This approach removes the need for global matching performance by the vision front-end - instead it must only pick the best match within any short sequence of images. The approach is applicable over environment changes that render traditional feature-based techniques ineffective. Using two car-mounted camera datasets we demonstrate the effectiveness of the algorithm and compare it to one of the most successful feature-based SLAM algorithms, FAB-MAP. The perceptual change in the datasets is extreme; repeated traverses through environments during the day and then in the middle of the night, at times separated by months or years and in opposite seasons, and in clear weather and extremely heavy rain. While the feature-based method fails, the sequence-based algorithm is able to match trajectory segments at 100% precision with recall rates of up to 60%.

Underwater Optical Wireless Communications, Networking, and Localization: A Survey

Abstract: Underwater wireless communications can be carried out through acoustic, radio frequency (RF), and optical waves. Compared to its bandwidth limited acoustic and RF counterparts, underwater optical wireless communications (UOWCs) can support higher data rates at low latency levels. However, the severe aquatic channel conditions (e.g., absorption, scattering, turbulence, etc.) pose great challenges for UOWCs and significantly reduce the attainable communication ranges, which necessitates efficient networking and localization solutions. Therefore, we provide a comprehensive survey on the challenges, advances, and prospects of underwater optical wireless networks (UOWNs) from a layer by layer perspective which includes: (1) Physical layer issues including propagation characteristics, channel modeling, and modulation techniques (2) Data link layer problems covering link configurations, link budgets, performance metrics, and multiple access schemes; (3) Network layer topics containing relaying techniques and potential routing algorithms; (4) Transport layer subjects such as connectivity, reliability, flow and congestion control; (5) Application layer goals, and (6) Localization and its impacts on UOWN layers. Finally, we outline the open research challenges and point out the prospective directions for underwater optical wireless communications, networking, and localization studies.

Control of a Wheelchair in an Indoor Environment Based on a Brain–Computer Interface and Automated Navigation

Abstract: The concept of controlling a wheelchair using brain signals is promising. However, the continuous control of a wheelchair based on unstable and noisy electroencephalogram signals is unreliable and generates a significant mental burden for the user. A feasible solution is to integrate a brain–computer interface (BCI) with automated navigation techniques. This paper presents a brain-controlled intelligent wheelchair with the capability of automatic navigation. Using an autonomous navigation system, candidate destinations and waypoints are automatically generated based on the existing environment. The user selects a destination using a motor imagery (MI)-based or P300-based BCI. According to the determined destination, the navigation system plans a short and safe path and navigates the wheelchair to the destination. During the movement of the wheelchair, the user can issue a stop command with the BCI. Using our system, the mental burden of the user can be substantially alleviated. Furthermore, our system can adapt to changes in the environment. Two experiments based on MI and P300 were conducted to demonstrate the effectiveness of our system.

Intelligent Bézier curve-based path planning model using Chaotic Particle Swarm Optimization algorithm

Abstract: Path planning algorithms have been used in different applications with the aim of finding a suitable collision-free path which satisfies some certain criteria such as the shortest path length and smoothness; thus, defining a suitable curve to describe path is essential. The main goal of these algorithms is to find the shortest and smooth path between the starting and target points. This paper makes use of a Bezier curve-based model for path planning. The control points of the Bezier curve significantly influence the length and smoothness of the path. In this paper, a novel Chaotic Particle Swarm Optimization (CPSO) algorithm has been proposed to optimize the control points of Bezier curve, and the proposed algorithm comes in two variants: CPSO-I and CPSO-II. Using the chosen control points, the optimum smooth path that minimizes the total distance between the starting and ending points is selected. To evaluate the CPSO algorithm, the results of the CPSO-I and CPSO-II algorithms are compared with the standard PSO algorithm. The experimental results proved that the proposed algorithm is capable of finding the optimal path. Moreover, the CPSO algorithm was tested against different numbers of control points and obstacles, and the CPSO algorithm achieved competitive results.