RGB-D SLAM Using Point–Plane Constraints for Indoor Environments

Guo, Ruibin; Peng, Keju; Wang, Fan; Zhai, Yongping; Liu, Yunhui

doi:10.3390/s19122721

Cited by 16 publications

(9 citation statements)

References 34 publications

(42 reference statements)

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“…Furthermore, the communal problem of the above studies is they all used the over-parameterised Hessian form to represent the Plane feature. In the meantime, the [16] and [17] define a plane with plane azimuth , plane elevation and the distance from the origin to the plane norm. But the azimuth and elevation way also need to be transformed to Hessian form during the optimisation procedure.…”

Section: Related Workmentioning

confidence: 99%

An Enhanced Visual SLAM Supported by the Integration of Plane Features for the Indoor Environment

Wang

Santos

et al. 2022

2022 IEEE 12th International Conference on Indoor Positioning and Indoor Navigation (IPIN)

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This paper presents an enhanced indoor RGB-D simultaneously localisation and mapping (SLAM) system based on the integration of plane and point features. A new method was proposed to register each point feature to a corresponding plane feature and then modify its position accordingly. The plane features are parallelly extracted from depth data sources and used jointly to solve the camera pose with point features. Plane features are stored on the map as the same as point features. Both point and plane features are used for backend optimisation, where the weights associated with features can be dynamically updated. At the same time, the on-plane feature points are fixed during the optimisation. The proposed method has been tested with open-source benchmarks, including the scenarios with or without a structured environment. Experiment results demonstrated that the proposed algorithm performs better than other widely cited visual SLAM systems in some structured environments.

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Section: Related Workmentioning

confidence: 99%

An Enhanced Visual SLAM Supported by the Integration of Plane Features for the Indoor Environment

Wang

Santos

et al. 2022

2022 IEEE 12th International Conference on Indoor Positioning and Indoor Navigation (IPIN)

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“…Zhou et al [24] utilize mean-shift to track dominant directions of MW and achieve drift-free rotation by decoupling the estimation of rotation and translation. Some other works [25,26,27] also exploit planes of MW to estimate drift-free rotation. These algorithms work well in some specific scenes, but they are also easy to fail because the MW assumption is not valid for some scenes.…”

Section: Related Workmentioning

confidence: 99%

Point-Plane SLAM Using Supposed Planes for Indoor Environments

Zhang

Wang

et al. 2019

Sensors

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Simultaneous localization and mapping (SLAM) is a fundamental problem for various applications. For indoor environments, planes are predominant features that are less affected by measurement noise. In this paper, we propose a novel point-plane SLAM system using RGB-D cameras. First, we extract feature points from RGB images and planes from depth images. Then plane correspondences in the global map can be found using their contours. Considering the limited size of real planes, we exploit constraints of plane edges. In general, a plane edge is an intersecting line of two perpendicular planes. Therefore, instead of line-based constraints, we calculate and generate supposed perpendicular planes from edge lines, resulting in more plane observations and constraints to reduce estimation errors. To exploit the orthogonal structure in indoor environments, we also add structural (parallel or perpendicular) constraints of planes. Finally, we construct a factor graph using all of these features. The cost functions are minimized to estimate camera poses and global map. We test our proposed system on public RGB-D benchmarks, demonstrating its robust and accurate pose estimation results, compared with other state-of-the-art SLAM systems.

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“…DSO is a direct sparse visual odometry algorithm, which combines a fully direct probabilistic model with joint optimization of all model parameters. In addition to points features, Visual SLAM for point-line [14] or point-plane [15] features has been studied for many years. Next, we will discuss the related work on omnidirectional odometry and SLAM.…”

Section: Related Workmentioning

confidence: 99%

Accurate and Robust Monocular SLAM with Omnidirectional Cameras

Liu

Guo

Feng

et al. 2019

Sensors

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Simultaneous localization and mapping (SLAM) are fundamental elements for many emerging technologies, such as autonomous driving and augmented reality. For this paper, to get more information, we developed an improved monocular visual SLAM system by using omnidirectional cameras. Our method extends the ORB-SLAM framework with the enhanced unified camera model as a projection function, which can be applied to catadioptric systems and wide-angle fisheye cameras with 195 degrees field-of-view. The proposed system can use the full area of the images even with strong distortion. For omnidirectional cameras, a map initialization method is proposed. We analytically derive the Jacobian matrices of the reprojection errors with respect to the camera pose and 3D position of points. The proposed SLAM has been extensively tested in real-world datasets. The results show positioning error is less than 0.1% in a small indoor environment and is less than 1.5% in a large environment. The results demonstrate that our method is real-time, and increases its accuracy and robustness over the normal systems based on the pinhole model.

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RGB-D SLAM Using Point–Plane Constraints for Indoor Environments

Cited by 16 publications

References 34 publications

An Enhanced Visual SLAM Supported by the Integration of Plane Features for the Indoor Environment

An Enhanced Visual SLAM Supported by the Integration of Plane Features for the Indoor Environment

Point-Plane SLAM Using Supposed Planes for Indoor Environments

Accurate and Robust Monocular SLAM with Omnidirectional Cameras

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