Real-Time Monocular Visual Odometry for Turbid and Dynamic Underwater Environments

Ferrera, Maxime; Moras, Julien; Trouvé-Peloux, Pauline; Creuze, Vincent

doi:10.3390/s19030687

Cited by 51 publications

(31 citation statements)

References 65 publications

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“…The feature points satisfying the motion estimation of the camera are static features and the others are considered as dynamic features. Maxime et al [19] propose real-time monocular visual odometry for dynamic underwater environments. They utilize RANSAC to compute the essential matrix and eliminate the outliers that are not consistent with the epipolar geometry.…”

Section: Dynamic Slammentioning

confidence: 99%

DM-SLAM: A Feature-Based SLAM System for Rigid Dynamic Scenes

Cheng

Wang

Zhou

et al. 2020

IJGI

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Most Simultaneous Localization and Mapping (SLAM) methods assume that environments are static. Such a strong assumption limits the application of most visual SLAM systems. The dynamic objects will cause many wrong data associations during the SLAM process. To address this problem, a novel visual SLAM method that follows the pipeline of feature-based methods called DM-SLAM is proposed in this paper. DM-SLAM combines an instance segmentation network with optical flow information to improve the location accuracy in dynamic environments, which supports monocular, stereo, and RGB-D sensors. It consists of four modules: semantic segmentation, ego-motion estimation, dynamic point detection and a feature-based SLAM framework. The semantic segmentation module obtains pixel-wise segmentation results of potentially dynamic objects, and the ego-motion estimation module calculates the initial pose. In the third module, two different strategies are presented to detect dynamic feature points for RGB-D/stereo and monocular cases. In the first case, the feature points with depth information are reprojected to the current frame. The reprojection offset vectors are used to distinguish the dynamic points. In the other case, we utilize the epipolar constraint to accomplish this task. Furthermore, the static feature points left are fed into the fourth module. The experimental results on the public TUM and KITTI datasets demonstrate that DM-SLAM outperforms the standard visual SLAM baselines in terms of accuracy in highly dynamic environments.

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Section: Dynamic Slammentioning

confidence: 99%

DM-SLAM: A Feature-Based SLAM System for Rigid Dynamic Scenes

Cheng

Wang

Zhou

et al. 2020

IJGI

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“…Under the water, because of the complexity of the environment and thus difficulty to obtain very accurate 3D measurements, comparative studies have been focused on simulated trajectories (Duarte et al, 2016) or on comparing real-time results with photogrammetrically derived trajectories obtained offline, in post processing, using for example BINGO software or Agisoft Photoscan (Drap et al, 2015;Nawaf et al, 2018) or Colmap structure from motion application (Ferrera et al, 2019). A real accuracy assessment of visual odometry and SLAM techniques, evaluated against a ground truth surveyed using an independent and more accurate method is still missing.…”

Section: Motivations and Aimmentioning

confidence: 99%

“…Under the water, SLAM techniques have been used to fuse inertial and acoustic positioning systems in particular in subsea metrology industry (IMCA, 2017), or for autonomous underwater robot navigation and localization using imaging sonar and visual sensors. First methods were focused on using acoustic images (Fusiello et al, 1999;Castellani et al, 2005;Roman 2005, Clark et al, 2008Ribas et al, 2009) to move then to visual based methods (Eustice et al, 2008;Kim and Eustice 2009;Duarte et al, 2016, Ferrera et al, 2019. A more comprehensive review of different techniques used for localization and mapping can be found in Paull et al (2014) and Hidalgo and Bräunl (2015).…”

Section: Slam Structure From Motion and Visual Odometrymentioning

confidence: 99%

“…Moreover, real-time centimetre accuracy positioning can be obtained only in confined areas, within the network of transponders, thus making a systematic mapping of larger areas time consuming and ineffective. On the other hand, vision-based localization techniques, such as visual odometry and Simultaneous Localization And Mapping (SLAM), are receiving more and more attention because of their significantly lower cost with respect to acoustic positioning methods (Eustice et al, 2008;Kim and Eustice 2009;Duarte et al, 2016, Ferrera et al, 2019. Moreover, open source frameworks are becoming publicly available, making the integration of such technology much easier.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Evaluation of Vision-Based Localization and Mapping Techniques in a Subsea Metrology Scenario

Menna¹,

Torresani²,

Nocerino³

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> Metrology is fundamental in all the applications that require to qualify, verify and validate measured data according to standards or, in other words, to assess their compliance with predefined tolerances. At sea, metrology is commonly associated with the process of measuring underwater structures, mainly pipeline elements widely used in offshore industry. Subsea operations are very expensive; optimizing time and money resources are the core factors driving innovation in the subsea metrology industry. In this study, the authors investigate the use of state-of-art vision-based algorithms, i.e. ORB-SLAM2 and Visual Odometry, as a navigation tool to assist and control a Remotely Operated Vehicle (ROV) while performing subsea metrology operations. In particular, the manuscript will focus on methods for assessing the accuracy of both trajectory and tie points provided by the tested approaches and evaluating whether the preliminary real time reconstruction meets the tolerances defined in typical subsea metrology scenarios.</p>

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“…They provide a wide range of utilities, from marine investigation [1,2], to archaeology [3] or structures monitoring [4,5]. Particularly interesting is the use of computer vision for underwater robot localization [6], for which expensive sensors based on acoustic technologies have been mainly used to date [7].…”

Section: Introductionmentioning

confidence: 99%

Generation and Processing of Simulated Underwater Images for Infrastructure Visual Inspection with UUVs

Álvarez-Tuñón

Jardón

Balaguer

2019

Sensors

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The development of computer vision algorithms for navigation or object detection is one of the key issues of underwater robotics. However, extracting features from underwater images is challenging due to the presence of lighting defects, which need to be counteracted. This requires good environmental knowledge, either as a dataset or as a physic model. The lack of available data, and the high variability of the conditions, makes difficult the development of robust enhancement algorithms. A framework for the development of underwater computer vision algorithms is presented, consisting of a method for underwater imaging simulation, and an image enhancement algorithm, both integrated in the open-source robotics simulator UUV Simulator. The imaging simulation is based on a novel combination of the scattering model and style transfer techniques. The use of style transfer allows a realistic simulation of different environments without any prior knowledge of them. Moreover, an enhancement algorithm that successfully performs a correction of the imaging defects in any given scenario for either the real or synthetic images has been developed. The proposed approach showcases then a novel framework for the development of underwater computer vision algorithms for SLAM, navigation, or object detection in UUVs.

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Real-Time Monocular Visual Odometry for Turbid and Dynamic Underwater Environments

Cited by 51 publications

References 65 publications

DM-SLAM: A Feature-Based SLAM System for Rigid Dynamic Scenes

DM-SLAM: A Feature-Based SLAM System for Rigid Dynamic Scenes

Evaluation of Vision-Based Localization and Mapping Techniques in a Subsea Metrology Scenario

Generation and Processing of Simulated Underwater Images for Infrastructure Visual Inspection with UUVs

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