Wide-Baseline Stereo-Based Obstacle Mapping for Unmanned Surface Vehicles

Mou, Xiaozheng; Wang, Han

doi:10.3390/s18041085

Cited by 19 publications

(13 citation statements)

References 18 publications

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“…Its ultimate purpose is to improve the level of ship automation and intelligence, and to ensure the safety, economy and comfort of a ship’s navigation [ 1 , 2 , 3 ]. Meanwhile, with the continuous development of technology and the extension of the field of ship application, the model of a traditional ship is developing towards miniaturization, speediness and intellectualization, and a new surface carrier has been developed—Unmanned Surface Vehicle (USV) [ 4 , 5 , 6 , 7 ]. To allow better maneuverability, the propulsion system of a USV has higher requirements.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Identification for Vector Propulsion of an Unmanned Surface Vehicle: Three Degrees of Freedom Model and Response Model

Wang

Fan

et al. 2018

Sensors

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This paper presents a complete scheme for research on the three degrees of freedom model and response model of the vector propulsion of an unmanned surface vehicle. The object of this paper is “Lanxin”, an unmanned surface vehicle (7.02 m × 2.6 m), which is equipped with a single vector propulsion device. First, the “Lanxin” unmanned surface vehicle and the related field experiments (turning test and zig-zag test) are introduced and experimental data are collected through various sensors. Then, the thrust of the vector thruster is estimated by the empirical formula method. Third, using the hypothesis and simplification, the three degrees of freedom model and the response model of USV are deduced and established, respectively. Fourth, the parameters of the models (three degrees of freedom model, response model and thruster servo model) are obtained by system identification, and we compare the simulated turning test and zig-zag test with the actual data to verify the accuracy of the identification results. Finally, the biggest advantage of this paper is that it combines theory with practice. Based on identified response model, simulation and practical course keeping experiments are carried out to further verify feasibility and correctness of modeling and identification.

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Section: Introductionmentioning

confidence: 99%

Modeling and Identification for Vector Propulsion of an Unmanned Surface Vehicle: Three Degrees of Freedom Model and Response Model

Wang

Fan

et al. 2018

Sensors

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“…Ranging with cameras is usually done with a stereo camera setup, where the two cameras used and the target form a triangle. Otherwise, monocular distance estimation methods exploit the camera movement, such as in [30], but these methods can only estimate the distance to static targets. More recently, methods which do not require camera movement have started to emerge, such as [31], [32].…”

Section: Visual Sensorsmentioning

confidence: 99%

Sensors and AI Techniques for Situational Awareness in Autonomous Ships: A Review

Thombre

Zhao

Ramm-Schmidt³

et al. 2022

IEEE Trans. Intell. Transport. Syst.

151

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Autonomous ships are expected to improve the level of safety and efficiency in future maritime navigation. Such vessels need perception for two purposes: to perform autonomous situational awareness and to monitor the integrity of the sensor system itself. In order to meet these needs, the perception system must fuse data from novel and traditional perception sensors using Artificial Intelligence (AI) techniques. This article overviews the recognized operational requirements that are imposed on regular and autonomous seafaring vessels, and then proceeds to consider suitable sensors and relevant AI techniques for an operational sensor system. The integration of four sensors families is considered: sensors for precise absolute positioning (Global Navigation Satellite System (GNSS) receivers and Inertial Measurement Unit (IMU)), visual sensors (monocular and stereo cameras), audio sensors (microphones), and sensors for remotesensing (RADAR and LiDAR). Additionally, sources of auxiliary data, such as Automatic Identification System (AIS) and external data archives are discussed. The perception tasks are related to well-defined problems, such as situational abnormality detection, vessel classification, and localization, that are solvable using AI techniques. Machine learning methods, such as deep learning and Gaussian processes, are identified to be especially relevant for these problems. The different sensors and AI techniques are characterized keeping in view the operational requirements, and some example state-of-the-art options are compared based on accuracy, complexity, required resources, compatibility and adaptability to maritime environment, and especially towards practical realization of autonomous systems.

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“…Many efforts, such as the direct method, semi-direct method and feature-based method, have been made for different visual ego-motion formulations [15]. Global map optimization techniques like loop closure, bundle adjustment (BA) and pose graph optimization were also proposed to make better the overall performance, which adjust the estimation results by taking into account the entire observation equations and eventual constraints [16].…”

Section: Motivations and Technical Challengesmentioning

confidence: 99%

“…In the problem, the RANSAC method is employed to remove outliers. The 3D-to-2D feature pairs from both cameras are all considered in the same optimization function, shown in Equation (16), which tries to minimize the reprojection error of the images and will concern the rigid constraint of the system.…”

Section: Calculation Of Covariance Matrixmentioning

confidence: 99%

Forward and Backward Visual Fusion Approach to Motion Estimation with High Robustness and Low Cost

et al. 2019

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We present a novel low-cost visual odometry method of estimating the ego-motion (self-motion) for ground vehicles by detecting the changes that motion induces on the images. Different from traditional localization methods that use differential global positioning system (GPS), precise inertial measurement unit (IMU) or 3D Lidar, the proposed method only leverage data from inexpensive visual sensors of forward and backward onboard cameras. Starting with the spatial-temporal synchronization, the scale factor of backward monocular visual odometry was estimated based on the MSE optimization method in a sliding window. Then, in trajectory estimation, an improved two-layers Kalman filter was proposed including orientation fusion and position fusion . Where, in the orientation fusion step, we utilized the trajectory error space represented by unit quaternion as the state of the filter. The resulting system enables high-accuracy, low-cost ego-pose estimation, along with providing robustness capability of handing camera module degradation by automatic reduce the confidence of failed sensor in the fusion pipeline. Therefore, it can operate in the presence of complex and highly dynamic motion such as enter-in-and-out tunnel entrance, texture-less, illumination change environments, bumpy road and even one of the cameras fails. The experiments carried out in this paper have proved that our algorithm can achieve the best performance on evaluation indexes of average in distance (AED), average in X direction (AEX), average in Y direction (AEY), and root mean square error (RMSE) compared to other state-of-the-art algorithms, which indicates that the output results of our approach is superior to other methods.

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Wide-Baseline Stereo-Based Obstacle Mapping for Unmanned Surface Vehicles

Cited by 19 publications

References 18 publications

Modeling and Identification for Vector Propulsion of an Unmanned Surface Vehicle: Three Degrees of Freedom Model and Response Model

Modeling and Identification for Vector Propulsion of an Unmanned Surface Vehicle: Three Degrees of Freedom Model and Response Model

Sensors and AI Techniques for Situational Awareness in Autonomous Ships: A Review

Forward and Backward Visual Fusion Approach to Motion Estimation with High Robustness and Low Cost

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