Stereo Vision Robot Obstacle Detection Based on the SIFT

Shao, Nuan; Li, Huiguang; Liu, Le; Zhang, Zhanling

doi:10.1109/gcis.2010.168

Cited by 6 publications

(4 citation statements)

References 5 publications

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“…Result is shown in Figure 3. We match the images after correction by improved SIFT algorithm [5][6], shown in Figure 4. …”

Section: A Binocular Vision Testmentioning

confidence: 99%

Study on mobile robot mapping based on binocular vision and Voronoi diagram

2011

2011 International Conference on Electrical and Control Engineering

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In this paper, we determine the three-dimensional coordinates of the obstacle with binocular vision sensor based on the application of improved SIFT (Scale-invariant feature transform) algorithm, the data will be treated as the the model generator of Voronoi diagram to complete the global map building. The biggest advantage of the V diagram is that the environmental path road is based on the vertical of the discrete centers of obstacles, which may make sure that the robot can keep away from the obstructions during operation. The experimental result shows that the map after construction demonstrate high reliability which consistent with the requirements of navigation.

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“…Result is shown in Figure 3. We match the images after correction by improved SIFT algorithm [5][6], shown in Figure 4. …”

Section: A Binocular Vision Testmentioning

confidence: 99%

Study on mobile robot mapping based on binocular vision and Voronoi diagram

2011

2011 International Conference on Electrical and Control Engineering

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“…These vehicles utilize binocular stereo vision for road environment detection and focus on addressing challenging visual perception issues in complex environments. Silicon Valley chip company Ambarella has developed binocular ADAS (Advanced Driver Assistance Systems) and autonomous driving chips [ 21 ], as well as binocular stereo-specific chips and solutions tailored to binocular vision systems, which act as valuable complements to integrated chips. These specialized chips can handle a portion of the perception tasks at the edge, creating a smaller and more efficient perception-decision loop.…”

Section: Introductionmentioning

confidence: 99%

A Binocular Stereo-Imaging-Perception System with a Wide Field-of-View and Infrared- and Visible Light-Dual-Band Fusion

Zhu,

Zhang,

Zhou

et al. 2024

Sensors

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With the continuous evolution of autonomous driving and unmanned driving systems, traditional limitations such as a limited field-of-view, poor ranging accuracy, and real-time display are becoming inadequate to satisfy the requirements of binocular stereo-perception systems. Firstly, we designed a binocular stereo-imaging-perception system with a wide-field-of-view and infrared- and visible light-dual-band fusion. Secondly we proposed a binocular stereo-perception optical imaging system with a wide field-of-view of 120.3°, which solves the small field-of-view of current binocular stereo-perception systems. Thirdly, For image aberration caused by the wide-field-of-view system design, we propose an ellipsoidal-image-aberration algorithm with a low consumption of memory resources and no loss of field-of-view. This algorithm simultaneously solves visible light and infrared images with an aberration rate of 45% and 47%, respectively. Fourthly, a multi-scale infrared- and visible light-image-fusion algorithm is used, which improves the situational-awareness capabilities of a binocular stereo-sensing system in a scene and enhances image details to improve ranging accuracy. Furthermore, this paper is based on the Taylor model-calibration binocular stereo-sensing system of internal and external parameters for limit correction; the implemented algorithms are integrated into an NVIDIA Jetson TX2 + FPGA hardware framework, enabling near-distance ranging experiments. The fusion-ranging accuracy within 20 m achieved an error of 0.02 m, outperforming both visible light- and infrared-ranging methods. It generates the fusion-ranging-image output with a minimal delay of only 22.31 ms at a frame rate of 50 Hz.

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“…The Scale Invariant Feature Transform (SIFT) algorithm [13] , possessing the advantage of high robustness, has always been taken as the preferred algorithm for stereo vision and 3D scene reconstruction. Shao et al [14] proposed a binocular stereo vision calibration method based on the parallel optical axis. In their study, feature matching algorithm was used to extract pixel coordinates of matching points to measure the obstacle distances.…”

Section: Introductionmentioning

confidence: 99%

Depth recovery for unstructured farmland road image using an improved SIFT algorithm

Yao¹,

Dong²,

Yang³

et al. 2019

International Journal of Agricultural and Biological Engineering

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Road visual navigation relies on accurate road models. This study was aimed at proposing an improved scale-invariant feature transform (SIFT) algorithm for recovering depth information from farmland road images, which would provide a reliable path for visual navigation. The mean image of pixel value in five channels (R, G, B, S and V) were treated as the inspected image and the feature points of the inspected image were extracted by the Canny algorithm, for achieving precise location of the feature points and ensuring the uniformity and density of the feature points. The mean value of the pixels in 5×5 neighborhood around the feature point at an interval of 45º in eight directions was then treated as the feature vector, and the differences of the feature vectors were calculated for preliminary matching of the left and right image feature points. In order to achieve the depth information of farmland road images, the energy method of feature points was used for eliminating the mismatched points. Experiments with a binocular stereo vision system were conducted and the results showed that the matching accuracy and time consuming for depth recovery when using the improved SIFT algorithm were 96.48% and 5.6 s, respectively, with the accuracy for depth recovery of-7.17%-2.97% in a certain sight distance. The mean uniformity, time consuming and matching accuracy for all the 60 images under various climates and road conditions were 50%-70%, 5.0-6.5 s, and higher than 88%, respectively, indicating that performance for achieving the feature points (e.g., uniformity, matching accuracy, and algorithm real-time) of the improved SIFT algorithm were superior to that of conventional SIFT algorithm. This study provides an important reference for navigation technology of agricultural equipment based on machine vision.

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Stereo Vision Robot Obstacle Detection Based on the SIFT

Cited by 6 publications

References 5 publications

Study on mobile robot mapping based on binocular vision and Voronoi diagram

Study on mobile robot mapping based on binocular vision and Voronoi diagram

A Binocular Stereo-Imaging-Perception System with a Wide Field-of-View and Infrared- and Visible Light-Dual-Band Fusion

Depth recovery for unstructured farmland road image using an improved SIFT algorithm

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