RGB-D SLAM with Structural Regularities

Li, Yanyan; Yunus, Raza; Brasch, Nikolas; Navab, Nassir; Tombari, Federico

doi:10.1109/icra48506.2021.9561560

Cited by 57 publications

(45 citation statements)

References 38 publications

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“…Based on keyframe management, a global dense planar map can be reconstructed using only a single CPU [19]. Planes can also be combined with keypoints and lines [2], [1] for more robust camera tracking. In structured environments, planes have been demonstrated to significantly reduce accumulated drift by minimising the rotational error between each frame and the underlying Manhattan World (MW) frame [1].…”

Section: B Planar Slammentioning

confidence: 99%

“…In indoor environments, planes are common man-made features. Planar SLAM methods have used the characteristics of planes to reduce long-term drift and improve accuracy of localisation [1], [2]. However, these methods assume that the environment is static -an assumption that is violated when the robot works in conjunction with other humans or robots, or manipulates objects in semi-automated warehouses.…”

Section: Introductionmentioning

confidence: 99%

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RGB-D SLAM in Indoor Planar Environments with Multiple Large Dynamic Objects

Long¹,

Rauch²,

Zhang³

et al. 2022

Preprint

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This work presents a novel dense RGB-D SLAM approach for dynamic planar environments that enables simultaneous multi-object tracking, camera localisation and background reconstruction. Previous dynamic SLAM methods either rely on semantic segmentation to directly detect dynamic objects; or assume that dynamic objects occupy a smaller proportion of the camera view than the static background and can, therefore, be removed as outliers. Our approach, however, enables dense SLAM when the camera view is largely occluded by multiple dynamic objects with the aid of camera motion prior. The dynamic planar objects are separated by their different rigid motions and tracked independently. The remaining dynamic non-planar areas are removed as outliers and not mapped into the background. The evaluation demonstrates that our approach outperforms the state-of-the-art methods in terms of localisation, mapping, dynamic segmentation and object tracking. We also demonstrate its robustness to large drift in the camera motion prior.

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Section: B Planar Slammentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

RGB-D SLAM in Indoor Planar Environments with Multiple Large Dynamic Objects

Long¹,

Rauch²,

Zhang³

et al. 2022

Preprint

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“…[11] allows the use of mean-shift algorithm for monocular scenes, by estimating surface normals from an RGB image using a convolutional neural network. [12] further improves translation estimation by tracking plane features, in addition to points and lines, and adding parallel and perpendicular constraints between them.…”

Section: Related Workmentioning

confidence: 99%

“…We also show the number of frames where MF tracking was used. Since ICL-NUIM is rendered based on a rigid Manhattan World model, MW-based methods work well, specially L-SLAM in of-kt0 and of-kt3 sequences and RGBD-SLAM [12] in lr-kt0 and of-kt2. However, MW-based methods are sensitive to the structure of environment as they need two perpendicular elements for every scene.…”

Section: A Pose Estimation 1) Icl-nuimmentioning

confidence: 99%

“…Another approach for drift reduction is the use of the Manhattan/Atlanta World assumption to estimate rotation, given the fact that drift is mostly driven by inaccurate rotation estimations [7], [8]. This technique has been employed by [9], [10] and our previous works [11], [12], which exploit parallel and perpendicular relationships between geometric 1 :Technical University of Munich, Germany; {raza.yunus, yanyan.li, federico.tombari}@tum.de 2 :Google Inc.…”

Section: Introductionmentioning

confidence: 99%

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ManhattanSLAM: Robust Planar Tracking and Mapping Leveraging Mixture of Manhattan Frames

Yunus¹,

Li²,

Tombari³

2021

Preprint

Self Cite

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In this paper, a robust RGB-D SLAM system is proposed to utilize the structural information in indoor scenes, allowing for accurate tracking and efficient dense mapping on a CPU. Prior works have used the Manhattan World (MW) assumption to estimate low-drift camera pose, in turn limiting the applications of such systems. This paper, in contrast, proposes a novel approach delivering robust tracking in MW and non-MW environments. We check orthogonal relations between planes to directly detect Manhattan Frames, modeling the scene as a Mixture of Manhattan Frames. For MW scenes, we decouple pose estimation and provide a novel drift-free rotation estimation based on Manhattan Frame observations. For translation estimation in MW scenes and full camera pose estimation in non-MW scenes, we make use of point, line and plane features for robust tracking in challenging scenes. Additionally, by exploiting plane features detected in each frame, we also propose an efficient surfel-based dense mapping strategy, which divides each image into planar and non-planar regions. Planar surfels are initialized directly from sparse planes in our map while non-planar surfels are built by extracting superpixels. We evaluate our method on public benchmarks for pose estimation, drift and reconstruction accuracy, achieving superior performance compared to other state-of-the-art methods. We will open-source our code in the future.

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RLP‐VIO: Robust and lightweight plane‐based visual‐inertial odometry for augmented reality

Zhou

Yang

et al. 2022

Computer Animation & Virtual

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We propose RLP-VIO-a robust and lightweight monocular visual-inertial odometry system using multiplane priors. With planes extracted from the point cloud, visual-inertial-plane PnP uses the plane information for fast localization. Depth estimation is susceptible to degenerated motion, so the planes are expanded in a reprojection consensus-based way robust to depth errors.For sensor fusion, our sliding-window optimization uses a novel structureless plane-distance error cost, which prevents the fill-in effect that poisons the BA problem's sparsity and permits the use of a smaller sliding window while maintaining good accuracy. The total computational cost is further reduced with our modified marginalization strategy. To further improve the tracking robustness, the landmark depths are constrained using the planes during degenerated motion. The whole system is parallelized with a three-stage pipeline. Under controlled environments, this parallelization runs deterministically and produces consistent results. The resulting VIO system is tested on widely used datasets and compared with several state-of-the-art systems. Our system achieves competitive accuracy and works robustly even on long and challenging sequences.To demonstrate the effectiveness of the proposed system, we also show the AR application running on mobile devices in real-time.

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RGB-D SLAM with Structural Regularities

Cited by 57 publications

References 38 publications

RGB-D SLAM in Indoor Planar Environments with Multiple Large Dynamic Objects

RGB-D SLAM in Indoor Planar Environments with Multiple Large Dynamic Objects

ManhattanSLAM: Robust Planar Tracking and Mapping Leveraging Mixture of Manhattan Frames

RLP‐VIO: Robust and lightweight plane‐based visual‐inertial odometry for augmented reality

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