Robotics: Science and Systems XI

doi:10.15607/rss.2015.xi

2015

DOI: 10.15607/rss.2015.xi

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Robotics: Science and Systems XI

Abstract: Abstract-We present a novel approach to real-time dense visual SLAM. Our system is capable of capturing comprehensive dense globally consistent surfel-based maps of room scale environments explored using an RGB-D camera in an incremental online fashion, without pose graph optimisation or any postprocessing steps. This is accomplished by using dense frame-tomodel camera tracking and windowed surfel-based fusion coupled with frequent model refinement through non-rigid surface deformations. Our approach applies l… Show more

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Cited by 5 publications

References 24 publications

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Multi-sensor fusion techniques for state estimation of micro air vehicles

et al. 2016

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Aggressive flight of micro air vehicles (MAVs) in unstructured, GPS-denied environments poses unique challenges for estimation of vehicle pose and velocity due to the noise, delay, and drift in individual sensor measurements. Maneuvering flight at speeds in excess of 5 m/s poses additional challenges even for active range sensors; in the case of LIDAR, an assembled scan of the vehicles environment will in most cases be obsolete by the time it is processed. Multi-sensor fusion techniques which combine inertial measurements with passive vision techniques and/or LIDAR have achieved breakthroughs in the ability to maintain accurate state estimates without the use of external positioning sensors. In this paper, we survey algorithmic approaches to exploiting sensors with a wide range of nonlinear dynamics using filter and bundle-adjustment based approaches for state estimation and optimal control. From this foundation, we propose a biologically-inspired framework for incorporating the human operator in the loop as a privileged sensor in a combined human/autonomy paradigm.

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Multi-sensor fusion techniques for state estimation of micro air vehicles

et al. 2016

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RGB-D dense SLAM with keyframe-based method

Sun

2018

Three-Dimensional Image Acquisition and Display Technology and Applications

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Currently, feature-based visual Simultaneous Localization and Mapping (SLAM) has reached a mature stage. Feature-based visual SLAM systems usually calculate the camera poses without producing a dense surface, even if a depth camera are provided. In contrast, dense SLAM systems simultaneously output camera poses as well as a dense surface of the reconstruction region. In this paper, we propose a new RGB-D dense SLAM system. First, camera pose is calculated by minimizing the combination of the reprojection error and the dense geometric error. We construct a new type of edge in g2o, which adds the extra constraints built with the dense geometric error to the graph optimization. The cost function is minimized in a coarse-to-fine strategy with GPU which contributes to the enhancement of system frame rate and promotion of large camera motion convergence. Second, in order to generate dense surfaces and provide users with a feedback of the scanned surfaces, we use the surfel model to fuse RGB-D streams and generated dense surface models in real-time. The surfels in the dense model are updated with embedded deformation graph to keep them consistent with the optimized camera poses after the system performs essential graph optimization and full Bundle Adjustment (BA). Third, a better 3D model is achieved by re-merging the stream with the optimized camera poses when the user ends the reconstruction. We compare the accuracy of generated camera trajectories and reconstruction surfaces with the state-of-the-art systems based on the TUM and ICL-NIUM RGB-D benchmark datasets. Experimental results show that the accuracy of dense surfaces produced online is very close to that of later re-fusion. And our system produces better results than the state-of-the-art systems in terms of the accuracy of the produced camera trajectories.

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Reducing the cost of visual DL datasets

Osteen¹,

Owens²,

Kaukeinen³

2019

Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications

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Robotics: Science and Systems XI

Cited by 5 publications

References 24 publications

Multi-sensor fusion techniques for state estimation of micro air vehicles

Multi-sensor fusion techniques for state estimation of micro air vehicles

RGB-D dense SLAM with keyframe-based method

Reducing the cost of visual DL datasets

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