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Register a new userHigh-precision target positioning system for unmanned vehicles based on binocular vision
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Added by
Xianqi He
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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Unmanned vehicles often need to locate targets with high precision during work. In the unmanned material handling workshop, the unmanned vehicle needs to perform high-precision pose estimation of the workpiece to accurately grasp the workpiece. In this context, this paper proposes a high-precision unmanned vehicle target positioning system based on binocular vision. The system uses a region-based stereo matching algorithm to obtain a disparity map, and uses the RANSAC algorithm to extract position and posture features, which achives the estimation of the position and attitude of a six-degree-of-freedom cylindrical workpiece. In order to verify the effect of the system, this paper collects the accuracy and calculation time of the output results of the cylinder in different poses. The experimental data shows that the position accuracy of the system is 0.61~1.17mm and the angular accuracy is 1.95~5.13{deg}, which can achieve better high-precision positioning effect.
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Unmanned ground vehicles have a huge development potential in both civilian and military fields, and have become the focus of research in various countries. In addition, high-precision, high-reliability sensors are significant for UGVs efficient operation. This paper proposes a brief review on sensor technologies for UGVs. Firstly, characteristics of various sensors are introduced. Then the strengths and weaknesses of different sensors as well as their application scenarios are compared. Furthermore, sensor applications in some existing UGVs are summarized. Finally, the hotspots of sensor technologies are forecasted to point the development direction.
The geometry of binocular projection is analyzed, with reference to the primate visual system. In particular, the effects of coordinated eye movements on the retinal images are investigated. An appropriate oculomotor parameterization is defined, and is shown to complement the classical version and vergence angles. The midline horopter is identified, and subsequently used to construct the epipolar geometry of the system. It is shown that the Essential matrix can be obtained by combining the epipoles with the projection of the midline horopter. A local model of the scene is adopted, in which depth is measured relative to a plane containing the fixation point. The binocular disparity field is given a symmetric parameterization, in which the unknown scene-depths determine the location of corresponding image-features. The resulting Cyclopean depth-map can be combined with the estimated oculomotor parameters, to produce a local representation of the scene. The recovery of visual direction and depth from retinal images is discussed, with reference to the relevant psychophysical and neurophysiological literature.
In recent years, many deep learning models have been adopted in autonomous driving. At the same time, these models introduce new vulnerabilities that may compromise the safety of autonomous vehicles. Specifically, recent studies have demonstrated that adversarial attacks can cause a significant decline in detection precision of deep learning-based 3D object detection models. Although driving safety is the ultimate concern for autonomous driving, there is no comprehensive study on the linkage between the performance of deep learning models and the driving safety of autonomous vehicles under adversarial attacks. In this paper, we investigate the impact of two primary types of adversarial attacks, perturbation attacks and patch attacks, on the driving safety of vision-based autonomous vehicles rather than the detection precision of deep learning models. In particular, we consider two state-of-the-art models in vision-based 3D object detection, Stereo R-CNN and DSGN. To evaluate driving safety, we propose an end-to-end evaluation framework with a set of driving safety performance metrics. By analyzing the results of our extensive evaluation experiments, we find that (1) the attacks impact on the driving safety of autonomous vehicles and the attacks impact on the precision of 3D object detectors are decoupled, and (2) the DSGN model demonstrates stronger robustness to adversarial attacks than the Stereo R-CNN model. In addition, we further investigate the causes behind the two findings with an ablation study. The findings of this paper provide a new perspective to evaluate adversarial attacks and guide the selection of deep learning models in autonomous driving.
In this paper, we propose a multi-target image tracking algorithm based on continuously apative mean-shift (Cam-shift) and unscented Kalman filter. We improved the single-lamp tracking algorithm proposed in our previous work to multi-target tracking, and achieved better robustness in the case of occlusion, the real-time performance to complete one positioning and relatively high accuracy by dynamically adjusting the weights of the multi-target motion states. Our previous algorithm is limited to the analysis of tracking error. In this paper, the results of the tracking algorithm are evaluated with the tracking error we defined. Then combined with the double-lamp positioning algorithm, the real position of the terminal is calculated and evaluated with the positioning error we defined. Experiments show that the defined tracking error is 0.61cm and the defined positioning error for 3-D positioning is 3.29cm with the average processing time of 91.63ms per frame. Even if nearly half of the LED area is occluded, the tracking error remains at 5.25cm. All of this shows that the proposed visible light positioning (VLP) method can track multiple targets for positioning at the same time with good robustness, real-time performance and accuracy. In addition, the definition and analysis of tracking errors and positioning errors indicates the direction for future efforts to reduce errors.
The hybrid electric system has good potential for unmanned tracked vehicles due to its excellent power and economy. Due to unmanned tracked vehicles have no traditional driving devices, and the driving cycle is uncertain, it brings new challenges to conventional energy management strategies. This paper proposes a novel energy management strategy for unmanned tracked vehicles based on local speed planning. The contributions are threefold. Firstly, a local speed planning algorithm is adopted for the input of driving cycle prediction to avoid the dependence of traditional vehicles on drivers operation. Secondly, a prediction model based on Convolutional Neural Networks and Long Short-Term Memory (CNN-LSTM) is proposed, which is used to process both the planned and the historical velocity series to improve the prediction accuracy. Finally, based on the prediction results, the model predictive control algorithm is used to realize the real-time optimization of energy management. The validity of the method is verified by simulation using collected data from actual field experiments of our unmanned tracked vehicle. Compared with multi-step neural networks, the prediction model based on CNN-LSTM improves the prediction accuracy by 20%. Compared with the traditional regular energy management strategy, the energy management strategy based on model predictive control reduces fuel consumption by 7%.
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