Homography-based depth recovery with descent images

Cai, Meng; Zhou, Na; Xue, Xiaoliang; Yang, Jia

doi:10.1007/s00138-013-0498-9

Cited by 9 publications

(18 citation statements)

References 27 publications

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“…Random sample consensus (RANSAC) [21] was used to estimate homography matrix (homography matrix is a mapping between two planes [22]) and removes mismatching points (matching points not corresponding to the same spatial point, where matching points means projections of the same spatial point on different images) to recover the Chang'e-3 lander's descent trajectory and map the landing area [23]. Meng [20] by utilizing the homography matrix in reconstructing the terrain of the landing area [24,25]. Di et al reconstructed the terrain of the landing area using descent images of Chang'e-3, but no detailed algorithm is mentioned in the paper [15].…”

Section: Introductionmentioning

confidence: 99%

“…Xiong et al realized depth map recovery with the plane-sweep method for unstructured scenes in descent images [19]. Meng et al modified the plane-sweep method by using the Zero-normalized cross-correlation score (ZNCC) method instead of the Sum of Squared Differences (SSD) to find the image correlation after warping, followed by combining best seed first propagation (BSFP) strategy with neighborhood value propagation to accomplish depth recovery [24,25].…”

Section: Introductionmentioning

confidence: 99%

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Trajectory Recovery and Terrain Reconstruction Based on Descent Images under Dual-Restrained Conditions: Tianwen-1

Chen

Liu

et al. 2022

Remote Sensing

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Tianwen-1 is the first Mars probe launched by China and the first mission in the world to successfully complete the three steps of exploration (orbiting, landing, and roving) at the one time. Based on the unverifiable descent images which cover the full range of the landing area, trajectory recovery and fine terrain reconstruction are important parts of the planetary exploration process. In this paper, a novel trajectory recovery and terrain reconstruction (TR-TR) algorithm employing descent images is proposed for the dual-restrained conditions: restraints of the flat terrain resulting in an unstable solution of the descent trajectory and of the parabolic descent trajectory causing low accuracy of terrain reconstruction, respectively. A landing simulation experiment on a landing field with Mars-like landform was carried out to test the robustness and feasibility of the algorithm. The experiment result showed that the horizontal error of the recovered trajectory didn’t exceed 0.397 m, and the elevation error of the reconstructed terrain was no more than 0.462 m. The algorithm successfully recovered the descent trajectory and generated high-resolution terrain products using in-orbit data of Tianwen-1, which provided effective support for the mission planning of the Zhurong rover. The analysis of the results indicated that the descent trajectory has parabolic properties. In addition, the reconstructed terrain contains abundant information and the vertical root mean square error (RMSE) of ground control points is smaller than 1.612 m. Terrain accuracy obtained by in-orbit data is lower than that obtained by field experiment. The work in this paper has made important contributions to the surveying and mapping of Tianwen-1 and has great application value.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Trajectory Recovery and Terrain Reconstruction Based on Descent Images under Dual-Restrained Conditions: Tianwen-1

Chen

Liu

et al. 2022

Remote Sensing

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“…In this case, we can make a good use of the planar constraint and solve the visual measurement problem by homography calibration. As stated in Hartley and Zisserman (2004), a homography matrix represents the reversible homogeneous transformation between two planes (Meng et al 2013). Once the homography is known, we can directly map image coordinates to object coordinates in the plane.…”

Section: Introductionmentioning

confidence: 99%

Global homography calibration for monocular vision-based pose measurement of mobile robots

Zhang

Wang

et al. 2017

Int J Intell Robot Appl

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Compared with the size of a calibration board, the inspected area within the camera field of view is much bigger in many applications such as visual surveillance systems. In this paper, we present a global image-to-ground homography calibration method to obtain the mapping between the image and the planar scene lying in the whole camera field of view. The calibration approach is proposed by fusing multiple local homography matrices, with each of them only reflecting the relationship between a small calibration board and its corresponding small image part. By further calibrating a height-related homography, visual measurement can be extended for arbitrary planar surfaces with known heights. The proposed method presents such merits as no requirement for camera internal parameters, high calibration accuracy, and ease of implementation. Experimental results show that (1) the proposed global homography method achieves more accurate measurement results than the local homography method; (2) the heightrelated homography is calibrated with high accuracy; (3) the proposed approach can be used for mobile robot localization with accuracy close to the performance limit of a monocular camera.

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“…Meng proposed a homography-based depth recovery method for descent images [13]. Joglekar presented a feature matching algorithm for the dense matching technique based on a probabilistic neural network [14].…”

Section: Introductionmentioning

confidence: 99%

An Optimized Method for Terrain Reconstruction Based on Descent Images

Zheng

et al. 2016

J. Eng. Technol. Sci.

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Abstract. An optimization method is proposed to perform high-accuracy terrain reconstruction of the landing area of Chang'e III. First, feature matching is conducted using geometric model constraints. Then, the initial terrain is obtained and the initial normal vector of each point is solved on the basis of the initial terrain. By changing the vector around the initial normal vector in small steps a set of new vectors is obtained. By combining these vectors with the direction of light and camera, the functions are set up on the basis of a surface reflection model. Then, a series of gray values is derived by solving the equations. The new optimized vector is recorded when the obtained gray value is closest to the corresponding pixel. Finally, the optimized terrain is obtained after iteration of the vector field. Experiments were conducted using the laboratory images and descent images of Chang'e III. The results showed that the performance of the proposed method was better than that of the classical feature matching method. It can provide a reference for terrain reconstruction of the landing area in subsequent moon exploration missions. Keywords:Chang'e III; descent images; feature point extraction; geometric constraint; terrain reconstruction. IntroductionIn lunar exploration, high-precision terrain reconstruction of the landing area has significance for achieving the scientific objectives of future projects and is important for safety and efficient path planning of the rover [1]. The Chang'e II detector acquired a large number of lunar images and other information in previous lunar exploration projects. Digital orthophoto maps with 1.5 m resolution and digital elevation models with 4 m resolution are currently being produced using the existing images captured by the Chang'e II satellite [2][3][4]. However, a terrain reconstruction with this accuracy can only be used for macro scientific investigation and cannot be used for path planning of the rover of Chang'e III [5]. The navigation cameras attached to the Chang'e III rover have a high resolution and can provide the basis for high-precision terrain reconstruction. However, the use of navigation cameras was not conducive to

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Homography-based depth recovery with descent images

Cited by 9 publications

References 27 publications

Trajectory Recovery and Terrain Reconstruction Based on Descent Images under Dual-Restrained Conditions: Tianwen-1

Trajectory Recovery and Terrain Reconstruction Based on Descent Images under Dual-Restrained Conditions: Tianwen-1

Global homography calibration for monocular vision-based pose measurement of mobile robots

An Optimized Method for Terrain Reconstruction Based on Descent Images

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