REMODE: Probabilistic, monocular dense reconstruction in real time

Pizzoli, Matia; Förster, Christian; Scaramuzza, Davide

doi:10.1109/icra.2014.6907233

Cited by 301 publications

(221 citation statements)

References 23 publications

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“…While direct image alignment is well-established for RGB-D or stereo sensors [14,4], only recently monocular direct VO algorithms have been proposed: In [24,20,21], accurate and fully dense depth maps are computed using a vari- Fig. 2: In addition to accurate, semi-dense 3D reconstructions, LSD-SLAM also estimates the associated uncertainty.…”

Section: Related Workmentioning

confidence: 99%

LSD-SLAM: Large-Scale Direct Monocular SLAM

Engel¹,

Schöps²,

Cremers³

2014

Lecture Notes in Computer Science

2,832

1,974

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Abstract. We propose a direct (feature-less) monocular SLAM algorithm which, in contrast to current state-of-the-art regarding direct methods, allows to build large-scale, consistent maps of the environment. Along with highly accurate pose estimation based on direct image alignment, the 3D environment is reconstructed in real-time as pose-graph of keyframes with associated semi-dense depth maps. These are obtained by filtering over a large number of pixelwise small-baseline stereo comparisons. The explicitly scale-drift aware formulation allows the approach to operate on challenging sequences including large variations in scene scale. Major enablers are two key novelties: (1) a novel direct tracking method which operates on sim(3), thereby explicitly detecting scale-drift, and (2) an elegant probabilistic solution to include the effect of noisy depth values into tracking. The resulting direct monocular SLAM system runs in real-time on a CPU.

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Section: Related Workmentioning

confidence: 99%

LSD-SLAM: Large-Scale Direct Monocular SLAM

Engel¹,

Schöps²,

Cremers³

2014

Lecture Notes in Computer Science

2,832

1,974

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“…To increase the robustness of this approach, it is proposed in [29] to explicitly model outliers. Furthermore, in [19], we showed how regularity in the depth map can be enforced by making use of a smoothness prior in regions characterized by high uncertainty.…”

Section: Probabilistic Monocular Depth Estimationmentioning

confidence: 99%

“…A similar model to estimate the depth of a pixel is used in [19,29]. To increase the robustness of this approach, it is proposed in [29] to explicitly model outliers.…”

Section: Probabilistic Monocular Depth Estimationmentioning

confidence: 99%

“…When observing demonstrations of monocular dense reconstruction from hand-held cameras, such as [17,19], one can notice the commonly used pattern of moving the camera in a circular trajectory around a reference view. 1 Intuitively, a circular trajectory constitutes a reasonable approach, as the generated epipolar lines span uniformly the images and increase the chances of reliable stereo matches.…”

Section: Introductionmentioning

confidence: 99%

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Appearance-based Active, Monocular, Dense Reconstruction for Micro Aerial Vehicles

Förster

Pizzoli

Scaramuzza

2014

Robotics: Science and Systems X

Self Cite

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Abstract-In this paper, we investigate the following problem: given the image of a scene, what is the trajectory that a robotmounted camera should follow to allow optimal dense depth estimation? The solution we propose is based on maximizing the information gain over a set of candidate trajectories. In order to estimate the information that we expect from a camera pose, we introduce a novel formulation of the measurement uncertainty that accounts for the scene appearance (i.e., texture in the reference view), the scene depth and the vehicle pose. We successfully demonstrate our approach in the case of realtime, monocular reconstruction from a micro aerial vehicle and validate the effectiveness of our solution in both synthetic and real experiments. To the best of our knowledge, this is the first work on active, monocular dense reconstruction, which chooses motion trajectories that minimize perceptual ambiguities inferred by the texture in the scene.

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“…2 In the past 5 years, many prominent research institutions began to develop advanced monocular visualbased simultaneous localisation and mapping (mSLAM) algorithms based on structure from motion (SFM) theory, [3][4][5][6][7][8][9][10][11] which are suitable to modern onboard embedded computers. Moreover, the visual scale problem, which was the main challenge of involving monocular vision into the control loop, has been addressed by fusing onboard inertial measurements (accelerometer and gyroscope), called the visualinertial navigation system (VINS).…”

Section: Introductionmentioning

confidence: 99%

Computationally efficient visual–inertial sensor fusion for Global Positioning System–denied navigation on a small quadrotor

Liu

Prior

Teacy

et al. 2016

Advances in Mechanical Engineering

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Because of the complementary nature of visual and inertial sensors, the combination of both is able to provide fast and accurate 6 degree-of-freedom state estimation, which is the fundamental requirement for robotic (especially, unmanned aerial vehicle) navigation tasks in Global Positioning System-denied environments. This article presents a computationally efficient visual-inertial fusion algorithm, by separating orientation fusion from the position fusion process. The algorithm is designed to perform 6 degree-of-freedom state estimation, based on a gyroscope, an accelerometer and a monocular visual-based simultaneous localisation and mapping algorithm measurement. It also recovers the visual scale for the monocular visual-based simultaneous localisation and mapping. In particular, the fusion algorithm treats the orientation fusion and position fusion as two separate processes, where the orientation fusion is based on a very efficient gradient descent algorithm, whereas the position fusion is based on a 13-state linear Kalman filter. The elimination of the magnetometer sensor avoids the problem of magnetic distortion, which makes it a power-on-and-go system once the accelerometer is factory calibrated. The resulting algorithm shows a significant computational reduction over the conventional extended Kalman filter, with competitive accuracy. Moreover, the separation between orientation and position fusion processes enables the algorithm to be easily implemented onto two individual hardware elements and thus allows the two fusion processes to be executed concurrently.

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REMODE: Probabilistic, monocular dense reconstruction in real time

Cited by 301 publications

References 23 publications

LSD-SLAM: Large-Scale Direct Monocular SLAM

LSD-SLAM: Large-Scale Direct Monocular SLAM

Appearance-based Active, Monocular, Dense Reconstruction for Micro Aerial Vehicles

Computationally efficient visual–inertial sensor fusion for Global Positioning System–denied navigation on a small quadrotor

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