OV$^{2}$SLAM: A Fully Online and Versatile Visual SLAM for Real-Time Applications

Ferrera, Maxime; Eudes, Alexandre; Moras, Julien; Sanfourche, Martial; Besnerais, Guy Le

doi:10.1109/lra.2021.3058069

Cited by 67 publications

(31 citation statements)

References 31 publications

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“…On the other hand, quick overviews of a scene, disaster or emergency response and other time-critical applications highly benefit from real-time capable processing workflows (Bu et al, 2016;Hein et al, 2019;Hinzmann et al, 2018). This paper extends our previous work on this topic (Fanta-Jende et al, 2020;Kern et al, 2020) by integrating and comparing various state-of-the-art SLAM implementations (ORB-SLAM3 (Campos et al, 2020), OV 2 SLAM (Ferrera et al, 2021) and (Sumikura et al, 2019)) and their impact on the quality and accuracy of generated data products, i.e. orthomosaics and surface models.…”

Section: Introductionmentioning

confidence: 76%

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An Accurate Real-Time Uav Mapping Solution for the Generation of Orthomosaics and Surface Models

Alexander¹,

Fanta-Jende

Glira

et al. 2021

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

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Abstract. UAVs have become an indispensable tool for a variety of mapping applications. Not only in the area of surveying, infrastructure planning and environmental monitoring tasks but also in time-critical applications, such as emergency and disaster response. Although UAVs enable rapid data acquisition per se, data processing usually relies on offline workflows. This contribution presents an accurate real-time data processing solution for UAV mapping applications as well as an extensive experimental and comparative study to the commercial offline solution Pix4D on the absolute accuracy of orthomosaics and digital surface models. We show that our procedure achieves an absolute horizontal and vertical accuracy of about 1 m without the use of ground control. The code will be made publicly available.

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Section: Introductionmentioning

confidence: 76%

“…At last, we integrated OV 2 SLAM (Ferrera et al, 2021) into the pipeline. It combines indirect, feature-based techniques for keyframes with a direct, photometric approach for frames in between.…”

Section: Visual Slam Varietiesmentioning

confidence: 99%

An Accurate Real-Time Uav Mapping Solution for the Generation of Orthomosaics and Surface Models

Alexander¹,

Fanta-Jende

Glira

et al. 2021

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

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“…In this work, we have used the open-source algorithm OV 2 SLAM 3 [10] to perform VSLAM on the video sequences. OV 2 SLAM uses optical flow [3] to track keypoints over the image sequences and use these tracks to both estimate the camera's poses and a 3D map of the imaged scene.…”

Section: Monocular Visual Slam For Trajectory and Sparse 3d Map Estimationmentioning

confidence: 99%

“…Note that we have used OV 2 SLAM here because we found out that it performs well on underwater data but in theory any other VSLAM method could be used. We refer the interested readers to [10] for more information on the algorithm. An example of the trajectory and 3D map that we estimate using the VSLAM algorithm is shown on the bottom-left of Fig.…”

Section: Monocular Visual Slam For Trajectory and Sparse 3d Map Estimationmentioning

confidence: 99%

Hyperspectral 3D Mapping of Underwater Environments

Ferrera

Arnaubec

Istenič

et al. 2021

2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)

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“…The rest of our evaluation incorporates this algorithm. [69] Single camera 6.23% 0.07 OV2SLAM [86] Stereo camera 0.94% 0.0023 MULLS [87] LiDAR 0.65% 0.0019…”

Section: Slam Algorithm Validationmentioning

confidence: 99%

SLAM-OR: Simultaneous Localization, Mapping and Object Recognition Using Video Sensors Data in Open Environments from the Sparse Points Cloud

Mazurek

Hachaj

2021

Sensors

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In this paper, we propose a novel approach that enables simultaneous localization, mapping (SLAM) and objects recognition using visual sensors data in open environments that is capable to work on sparse data point clouds. In the proposed algorithm the ORB-SLAM uses the current and previous monocular visual sensors video frame to determine observer position and to determine a cloud of points that represent objects in the environment, while the deep neural network uses the current frame to detect and recognize objects (OR). In the next step, the sparse point cloud returned from the SLAM algorithm is compared with the area recognized by the OR network. Because each point from the 3D map has its counterpart in the current frame, therefore the filtration of points matching the area recognized by the OR algorithm is performed. The clustering algorithm determines areas in which points are densely distributed in order to detect spatial positions of objects detected by OR. Then by using principal component analysis (PCA)—based heuristic we estimate bounding boxes of detected objects. The image processing pipeline that uses sparse point clouds generated by SLAM in order to determine positions of objects recognized by deep neural network and mentioned PCA heuristic are main novelties of our solution. In contrary to state-of-the-art approaches, our algorithm does not require any additional calculations like generation of dense point clouds for objects positioning, which highly simplifies the task. We have evaluated our research on large benchmark dataset using various state-of-the-art OR architectures (YOLO, MobileNet, RetinaNet) and clustering algorithms (DBSCAN and OPTICS) obtaining promising results. Both our source codes and evaluation data sets are available for download, so our results can be easily reproduced.

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OV$^{2}$SLAM: A Fully Online and Versatile Visual SLAM for Real-Time Applications

Cited by 67 publications

References 31 publications

An Accurate Real-Time Uav Mapping Solution for the Generation of Orthomosaics and Surface Models

An Accurate Real-Time Uav Mapping Solution for the Generation of Orthomosaics and Surface Models

Hyperspectral 3D Mapping of Underwater Environments

SLAM-OR: Simultaneous Localization, Mapping and Object Recognition Using Video Sensors Data in Open Environments from the Sparse Points Cloud

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