A Low-Cost Monocular Vision-Based Obstacle Avoidance Using SVM and Optical Flow
17 Dec 2018-Vol. 06, Iss: 04, pp 267-275
TL;DR: A novel algorithm is proposed, termed as Pyramid Histogram of Oriented Optical Flow ([Formula: see text]-HOOF), which distinctly captures motion vectors from local image patches and provides a robust descriptor capable of discriminating obstacles from nonobstacles.
Abstract: Development of low-cost robots with the capability to detect and avoid obstacles along their path is essential for autonomous navigation. These robots have limited computational resources and paylo...
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TL;DR: In this article, a distributed proximal gradient algorithm for non-smooth non-convex optimization problem over time-varying multi-agent networks is presented, where each agent updates local variable estimate by the multi-step consensus operator and the proximal operator.
Abstract: This note studies the distributed non-convex optimization problem with non-smooth regularization, which has wide applications in decentralized learning, estimation and control. The objective function is the sum of different local objective functions, which consist of differentiable (possibly non-convex) cost functions and non-smooth convex functions. This paper presents a distributed proximal gradient algorithm for the non-smooth non-convex optimization problem over time-varying multi-agent networks. Each agent updates local variable estimate by the multi-step consensus operator and the proximal operator. We prove that the generated local variables achieve consensus and converge to the set of critical points with convergence rate $O(1/T)$. Finally, we verify the efficacy of proposed algorithm by numerical simulations.
2 citations
04 May 2022
TL;DR: In this paper , a monocular vision measurement method for the non-horizontal target is proposed, based on the principle of camera imaging, internal and external parameters of the camera and analogue-to-digital conversion principle.
Abstract: With the development of computer vision technology, target measurement methods have been widely used in robot automatic obstacle avoidance, vehicle-assisted driving and other systems. There are many target measurement technologies available, but most of them are based on binocular or trinocular vision, or based on monocular vision with other auxiliary equipment, or based on monocular vision to measure horizontal target. The first two technologies achieve precise positioning by increasing the amount of data and sacrificing processing speed, while the third only studies the measurement method of the horizontal target. To address the complexity of the multi-equipment measurement methods and the limitation of the monocular measurement methods for horizontal target, this paper proposes a novel monocular vision measurement method for the non-horizontal target. According to the principle of camera imaging, internal and external parameters of the camera and analogue-to-digital conversion principle, the imaging relationship model for measuring the relative height and target distance of non-horizontal target is deduced, and the solvability of the model is demonstrated by mathematics. The experimental results verify the correctness and feasibility of this method.
1 citations
TL;DR: In this article , a distributed proximal gradient algorithm for nonsmooth nonconvex optimization problem is proposed, which updates local variable estimates with a constant step-size at the cost of multiple consensus steps, where the number of communication rounds increases over time.
Abstract: This article studies the distributed nonconvex optimization problem with nonsmooth regularization, which has wide applications in decentralized learning, estimation, and control. The objective function is the sum of local objective functions, which consist of differentiable (possibly nonconvex) cost functions and nonsmooth convex functions. This article presents a distributed proximal gradient algorithm for the nonsmooth nonconvex optimization problem. Over time-varying multiagent networks, the proposed algorithm updates local variable estimates with a constant step-size at the cost of multiple consensus steps, where the number of communication rounds increases over time. We prove that the generated local variables achieve consensus and converge to the set of critical points. Finally, we verify the efficiency of the proposed algorithm by numerical simulations.
1 citations
TL;DR: In this paper , a distributed proximal gradient algorithm for non-smooth non-convex optimization problem is proposed, which updates local variable estimates with a constant step-size at the cost of multiple consensus steps.
Abstract: This paper studies the distributed non-convex optimization problem with non-smooth regularization, which has wide applications in decentralized learning, estimation and control. The objective function is the sum of local objective functions, which consist of differentiable (possibly non-convex) cost functions and non-smooth convex functions. This paper presents a distributed proximal gradient algorithm for the non-smooth non-convex optimization problem. Over time-varying multi-agent networks, the proposed algorithm updates local variable estimates with a constant step-size at the cost of multiple consensus steps, where the number of communication rounds increases over time. We prove that the generated local variables achieve consensus and converge to the set of critical points. Finally, we verify the efficiency of the proposed algorithm by numerical simulations.
25 Jul 2022
TL;DR: Zhang et al. as discussed by the authors study the influence of random impulse noise in images on the localization accuracy of visual SLAM, and reduce these influences by denoising and removing mismatches.
Abstract: The localization accuracy of visual SLAM depends on the image quality. However, in postdisaster rescue missions, the images obtained by the camera often contain considerable noise, which affects the pose estimation based on visual SLAM. In this paper, we study the influence of random impulse noise in images on the localization accuracy of visual SLAM, and reduce these influences by denoising and removing mismatches. First, the camera image is preprocessed by the traditional image noise reduction method. Aiming at the problem of a large number of mismatches in optical flow tracking due to the influence of residual noise, the improved random sample consensus method is adopted to remove it. Preliminarily judge the correct matching probability of optical flow tracking results by normalized cross-correlation matching before random sampling. Then use guided sampling to select matching points to estimate the camera motion model, to increase the robustness of the SLAM system. Finally, our method is verified in the open-source solution VINS-Fusion. Experiments show that after random impulse noise is added to the KITTI dataset, the pose estimation accuracy of the improved SLAM is higher than the pose estimation accuracy after noise reduction only, and it is also higher than the pose estimation results of the original images in multiple sequences of the KITTI dataset.
References
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TL;DR: Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
Abstract: LIBSVM is a library for Support Vector Machines (SVMs). We have been actively developing this package since the year 2000. The goal is to help users to easily apply SVM to their applications. LIBSVM has gained wide popularity in machine learning and many other areas. In this article, we present all implementation details of LIBSVM. Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
40,826 citations
TL;DR: The computation of optical flow is investigated in this survey: widely known methods for estimating optical flow are classified and examined by scrutinizing the hypothesis and assumptions they use.
Abstract: Two-dimensional image motion is the projection of the three-dimensional motion of objects, relative to a visual sensor, onto its image plane. Sequences of time-orderedimages allow the estimation of projected two-dimensional image motion as either instantaneous image velocities or discrete image displacements. These are usually called the optical flow field or the image velocity field. Provided that optical flow is a reliable approximation to two-dimensional image motion, it may then be used to recover the three-dimensional motion of the visual sensor (to within a scale factor) and the three-dimensional surface structure (shape or relative depth) through assumptions concerning the structure of the optical flow field, the three-dimensional environment, and the motion of the sensor. Optical flow may also be used to perform motion detection, object segmentation, time-to-collision and focus of expansion calculations, motion compensated encoding, and stereo disparity measurement. We investigate the computation of optical flow in this survey: widely known methods for estimating optical flow are classified and examined by scrutinizing the hypothesis and assumptions they use. The survey concludes with a discussion of current research issues.
1,317 citations
TL;DR: In this article, an ultrasonic sensor that is able to measure the distance from the ground of selected points of a motor vehicle is described, which is based on the measurement of the time of flight of a ultrasonic pulse which is reflected by the ground.
Abstract: This paper describes an ultrasonic sensor that is able to measure the distance from the ground of selected points of a motor vehicle. The sensor is based on the measurement of the time of flight of an ultrasonic pulse, which is reflected by the ground. A constrained optimization technique is employed to obtain reflected pulses that are easily detectable by means of a threshold comparator. Such a technique, which takes the frequency response of the ultrasonic transducers into account, allows a sub-wavelength detection to be obtained. Experimental tests, performed with a 40 kHz piezoelectric-transducer based sensor, showed a standard uncertainty of 1 mm at rest or at low speeds; the sensor still works at speeds of up to 30 m/s, although at higher uncertainty. The sensor is composed of only low cost components, thus being apt for first car equipment in many cases, and is able to self-adapt to different conditions in order to give the best results.
298 citations
TL;DR: It is shown that a quantity termed the directional divergence of the 2-D motion field can be used as a reliable indicator of the presence of obstacles in the visual field of an observer undergoing generalized rotational and translational motion.
Abstract: The use of certain measures of flow field divergence is investigated as a qualitative cue for obstacle avoidance during visual navigation. It is shown that a quantity termed the directional divergence of the 2-D motion field can be used as a reliable indicator of the presence of obstacles in the visual field of an observer undergoing generalized rotational and translational motion. The necessary measurements can be robustly obtained from real image sequences. Experimental results are presented showing that the system responds as expected to divergence in real-world image sequences, and the use of the system to navigate between obstacles is demonstrated. >
256 citations
Proceedings Article•
01 Jan 1998TL;DR: In this paper, a number of elegant strategies that can be profitably applied to the design of autonomous robots are described, such as the "peering" behaviour of grasshoppers and the "centring" response of bees flying through a tunnel.
Abstract: Recent studies of insect visual behaviour and navigation reveal a number of elegant strategies that can be profitably applied to the design of autonomous robots. The “peering” behaviour of grasshoppers, for example, has inspired the design of new rangefinding systems. The “centring” response of bees flying through a tunnel has led to simple methods for navigating through corridors. These and other visually-mediated insect behaviours are described along with a number of applications to robot navigation.
191 citations