Semi-dense visual odometry for RGB-D cameras using approximate nearest neighbour fields

Zhou, Yi; Kneip, Laurent; Li, Hongdong

doi:10.1109/icra.2017.7989742

Cited by 13 publications

(4 citation statements)

References 25 publications

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“…A projection onto the local gradient direction g(η(x ijk , C j )) is added in order to facilitate optimisation in the sliding situation. Note that, as discussed in [35], this step also helps to efficiently overcome the bias discussed in [27] without having to employ the more expensive technique of variable lifting. To conclude, a robust norm µ(•) such as the Huber norm is added to account for outliers and missing data.…”

Section: Let Us Define the Vector Bmentioning

confidence: 99%

“…One of the more expensive parts of the computation is given by the nearest neighbour look-up η(x, C j ). Inspired by [35], we employ a simple solution to speed up the optimisation by pre-computing a nearest neighbour look-up field that indicates the nearest pixel on an edge for any pixel in the entire image. An example is given in Figure 3.…”

Section: Let Us Define the Vector Bmentioning

confidence: 99%

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Fully automatic structure from motion with a spline-based environment representation

Wang¹,

Kneip²

2018

Preprint

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While the common environment representation in structure from motion is given by a sparse point cloud, the community has also investigated the use of lines to better enforce the inherent regularities in man-made surroundings. Following the potential of this idea, the present paper introduces a more flexible higher-order extension of points that provides a general model for structural edges in the environment, no matter if straight or curved. Our model relies on linked Bézier curves, the geometric intuition of which proves great benefits during parameter initialization and regularization. We present the first fully automatic pipeline that is able to generate spline-based representations without any human supervision. Besides a full graphical formulation of the problem, we introduce both geometric and photometric cues as well as higher-level concepts such overall curve visibility and viewing angle restrictions to automatically manage the correspondences in the graph. Results prove that curve-based structure from motion with splines is able to outperform state-of-the-art sparse feature-based methods, as well as to model curved edges in the environment.

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Section: Let Us Define the Vector Bmentioning

confidence: 99%

Section: Let Us Define the Vector Bmentioning

confidence: 99%

Fully automatic structure from motion with a spline-based environment representation

Wang¹,

Kneip²

2018

Preprint

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“…SLAM (Simultaneous Localization and Mapping) [6] is a heated research area under computer vision and control during the past two decades, just aimed to realize the function mentioned above. It's widely used in moving-based machines such as robotics, drones, autonomous cars, as well as in applications which will require reconstruction of 3D environment, like VR/AR [7], and can be applied to various industries as gaming, interior design, manufacturing etc.…”

Section: Introductionmentioning

confidence: 99%

Outdoor real-time RGBD sensor fusion of stereo camera and sparse lidar

Kong

Zhang

2022

J. Phys.: Conf. Ser.

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Autonomous driving technology relies on high-resolution, high-performance vision sensing technology. As the two most popular sensors in the industry, Lidar and stereo cameras play an important role in sensing, detection, control and planning decisions. At this stage, the Lidar can obtain more accurate depth map information, but with the increase of resolution, its cost increases sharply and cannot be applied in large scale in the market. At the same time, the depth detection scheme based on computer vision has high resolution, but the data accuracy is very low. In order to solve the practical application defects of the above two sensors, this paper proposes a new sensor fusion method for stereo camera and low-resolution Lidar, which has high resolution, high performance and low cost. This paper adopts new sensor design, multi-sensor calibration, classification and selection of Lidar pointcloud features, large-scale and efficient stereo image matching and depth map calculation, and method of filling missing information based on pointcloud segmentation. To verify the effectiveness of the method in this paper, this paper used high-resolution Lidar as ground truth for comparison. The results show that the fusion method can achieve an average improved accuracy of 30% within a close range of 30 meters, while achieving 98% resolution. In addition, this paper presents a scheme for visualizing multi-sensor image fusion, and encapsulates five modules: multi-sensor calibration, large-scale stereo depth calculation, low-resolution Lidar simulation, sensor data fusion, and fusion image and error visualization, in order to facilitate future secondary development.

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“…Acquiring dense and accurate depth measurements is crucial for various applications such as autonomous driving [1], indoor navigation [2], robot SLAM [3], virtual or augmented reality [4]. However, due to the limitation of current depth sensing technology, captured depth maps are often in a sparse form (i.e.LiDAR) or suffering severe data missing problem on transparent and reflective surfaces (i.e.Microsoft Kinect, Intel RealSense, and Google Tango).…”

Section: Introductionmentioning

confidence: 99%

Efficient Depth Completion Using Learned Bases

Zhong,

Dai,

2020

Preprint

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In this paper, we propose a new global geometry constraint for depth completion. By assuming depth maps often lay on low dimensional subspaces, a dense depth map can be approximated by a weighted sum of full-resolution principal depth bases. The principal components of depth fields can be learned from natural depth maps. The given sparse depth points are served as a data term to constrain the weighting process. When the input depth points are too sparse, the recovered dense depth maps are often over smoothed. To address this issue, we add a colour-guided auto-regression model as another regularization term. It assumes the reconstructed depth maps should share the same nonlocal similarity in the accompanying colour image. Our colour-guided PCA depth completion method has closed-form solutions, thus can be efficiently solved and is significantly more accurate than PCA only method. Extensive experiments on KITTI and Middlebury datasets demonstrate the superior performance of our proposed method.

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Semi-dense visual odometry for RGB-D cameras using approximate nearest neighbour fields

Cited by 13 publications

References 25 publications

Fully automatic structure from motion with a spline-based environment representation

Fully automatic structure from motion with a spline-based environment representation

Outdoor real-time RGBD sensor fusion of stereo camera and sparse lidar

Efficient Depth Completion Using Learned Bases

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