Top PDF Landmark detection for vision based of autonomous guided vehicle

Landmark detection for vision based of autonomous guided vehicle

Landmark detection for vision based of autonomous guided vehicle

The reasons to build an AGV is to overcome the logistic problems in factory such as transferring raw materials or goods on time, reducing production lead time and labor cost. AGV can perform tiring job and reduce cost of diesel to run forklift during handling materials or goods. AGV help to deliver raw materials to the production line when require without human assist. Thus it prevents production to stop just because of out of stock. To ensure the AGV deliver the material to production line or other require place, the AGV must accurately locate it position in factory. In vision based navigation of AGV case, in order to fast and accurately locate the AGV, the landmarks must be detected and recognized by vision sensor accurately in a real-time (Barbera, H.M. and Perez, D.H., 2010). Therefore it is important to determine the suitability of landmarks and strategic place for the landmarks to be placed. Then AGV should be able to distinguish landmark from any obstacles and the obstacles might possibly have the same features with landmarks. In (Roborealm) it introduced landmark tracking of vision based robot based on color and rectangular shape. Unfortunately the landmark features is not robust enough. It only focuses on rectangular tracking without considering the exact size of landmark and orientation of landmark. Thus, this project attention is to develop a various shapes of landmarks to navigate vision based AGV.
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Development of vision autonomous guided vehicle behaviour using neural network

Development of vision autonomous guided vehicle behaviour using neural network

The experimental results from above research have shown that V-AVG navigation control system have been successfully implemented on the real guideline system. A low cost of USB camera can be use for vision based line recognition and detection algorithm. The USB camera has performed well in executing the proposed algorithm. This control system do not need the destination target to be programmed, it depends on the guideline.

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Deep learning for vision-based micro aerial vehicle autonomous landing

Deep learning for vision-based micro aerial vehicle autonomous landing

Our framework is motivated by the recent proposed detection model Yolo 17 and the neural network archi- tecture SqueezeNet 22 to achieve real-time landmark detection. The Yolo model frames object detection in terms of deep learning based regression for the purpose of determining spatially separated bounding boxes and associated class probabilities. The SqueezeNet aims at modeling a CNN with few parameters. In order to develop an effective end-to-end landmark detection system with implementation efficiency, we establish our CNN framework sharing advantages of the Yolo regression and the SqueezeNet efficient architecture. Specifically, our CNN-based landmark detection method regresses landmark positions directly from cap- tured raw images through a multilayer architecture such that the feature extraction and matching are indis- tinguishably integrated into an overall framework. Furthermore, the strong representational power of the CNN not only increases the adaptability of an autonomous landing system from one specific land- mark to multiple landmarks but also improves the detection robustness with respect to light variation.
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Deep learning for vision-based micro aerial vehicle autonomous landing

Deep learning for vision-based micro aerial vehicle autonomous landing

Our framework is motivated by the recent proposed detection model Yolo 17 and the neural network archi- tecture SqueezeNet 22 to achieve real-time landmark detection. The Yolo model frames object detection in terms of deep learning based regression for the purpose of determining spatially separated bounding boxes and associated class probabilities. The SqueezeNet aims at modeling a CNN with few parameters. In order to develop an effective end-to-end landmark detection system with implementation efficiency, we establish our CNN framework sharing advantages of the Yolo regression and the SqueezeNet efficient architecture. Specifically, our CNN-based landmark detection method regresses landmark positions directly from cap- tured raw images through a multilayer architecture such that the feature extraction and matching are indis- tinguishably integrated into an overall framework. Furthermore, the strong representational power of the CNN not only increases the adaptability of an autonomous landing system from one specific land- mark to multiple landmarks but also improves the detection robustness with respect to light variation.
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Vision-based Path Estimation for the Navigation of Autonomous Electric Vehicle

Vision-based Path Estimation for the Navigation of Autonomous Electric Vehicle

energy problems caused by the use of an internal combustion engine vehicle. Developing such vehicles for solving the environment and energy problems is a great idea. Currently, many researches publish technical papers in journals, which are related to autonomous EV. In their researches, steering wheel, brake and acceleration pedals are control by using computers [8-10]. On the other hand, users and drivers do not have a direct contact with them. A touch panel is installed in the EV and it serves a user Graphical User Interface (GUI) for users and drivers interact with controlling devices. Unfortunately, based on current outcomes more effort should be done for making sure that autonomous EV could move with safety. Although mechanism of mechanical could be used to solve safety and reliability issues of autonomous EV, computational approach also very important. The computational approach is for example the algorithm for controlling motor device, the capacity of data transmission device, image processing technique and etc. [11-13]. Autonomous vehicle with intelligent driving control are developed to provide the driver assistance as well as unmanned driver for road, logistics and flexible manufacturing system. It is an automatic guided vehicle and able to move automatically along the road. This research will focus on design and implementation of a sensor fusion system for navigation and control of an autonomous electric vehicle. It also introduced the intelligent vehicle trace, obstacle avoidance and speed control. In the first part, a vision system for the electric vehicle will be develop by using image processing techniques to recognize neighboring circumstances surrounding the electric vehicle.
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Robust convex optimisation techniques for autonomous vehicle vision-based navigation

Robust convex optimisation techniques for autonomous vehicle vision-based navigation

In practice, for any navigation system, errors in position estimates are continuously growing due to the integration of noisy measurements over time and imperfect computational techniques. This unavoidable drift in motion estimation, due to inherent inaccuracy of the devices as well, needs to be corrected. Thus, providing additional correction tools would have a crucial impact on the final estimates of the navigation solution. Indeed, after long navigation into an unknown environment, detecting that the vehicle has returned to a previously visited location offers the opportunity to correct and to increase the accuracy and the consistency of the vehicle motion estimates. In computer vision, this is known as detecting loop- closures. In Chapter 7, we present a novel appearance-based technique for visual loop-closure detection. The widely used techniques based on the Bag-of-Words image representation have shown some limitations, especially with the perceptual aliasing problem. Our solution, however, uses both local invariant and colour features. Moreover, the proposed solution combines Gaussian mixture modelling (GMM) with the KD-tree data structure. In doing so, this solution takes advantage of the robustness of the KD-tree data structure and the efficiency of the Gaussian mixture modelling representation. Experimental validation using datasets from different environments has been conducted. We show that due to their efficiency and complementarity, a combination of KD-trees with GMM could be an alternative for real-time loop-closure detection for mobile robots navigation.
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Curve Path Detection in Autonomous Vehicle using Deep Learning

Curve Path Detection in Autonomous Vehicle using Deep Learning

A GPS based urban navigation system, based on the context awareness technique was used to detect the vehicles and fallow the lane boundaries [5]. Lane quest is he technique or the system that uses the low-energy inertial sensors available in the smart phones to provide the accurate vehicle lane detection and from the smart phone sensors surrounding environment is detected for the vehicles and the lanes [6]. To identify the lane boundaries more accurate between the entry or exit and the lane change from one lane to another lane with colliding the other vehicles an radar based tracking identification technique developed by the goodness of fit (GOF) concept to identify the position of the vehicles in the curved paths [7]. Image processing and steering control technique was used in the lane and vehicle detection by Kevin Mc Fall [8]. A special radar named all-weather automotive millimetre wave (MMW) radar was used to describe the frequency modulation of a co- planar wave radar design is capable for identifying the obstacles in the filed view by Mark E. Russell, Arthur Crain, Anthony Curran, Richard A. Campbell, Clifford A. Drubin, and William F. Miccioli [9]. A monocular vision based lane detection is developed by using on board lane detection system
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Vision-Based Target Tracking and Autonomous Landing of a Quadrotor on a Ground Vehicle

Vision-Based Target Tracking and Autonomous Landing of a Quadrotor on a Ground Vehicle

tracking experiment, and 4 trials for the mobile target track- ing with the quadrotor flying at an altitude of 1.5 meters. The results are shown in Fig. 4 and Fig. 6, respectively. The 3D trajectories of the MAV and GV in the last trial of the respective experiments are also plotted in Fig. 5 and Fig. 7. In Fig. 4, all trials show satisfactory tracking performance although Trial 1 tracking controller was activated much later than the others. This is due to the displacement of the MAV during its climbing phase causing the GV to lie outside the onboard camera FOV when the MAV has reached its operating altitude. Once the GV was detected, the tracking controller was able to maintain target detection and drive the horizontal position error to zero after about 5 seconds. The descent controller was activated after t track reached 30 seconds.
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Landmark guided trajectory of an automated guided vehicle using omnidirectional vision

Landmark guided trajectory of an automated guided vehicle using omnidirectional vision

A review by DeSouza and Kak revealed that researches on vision based AGV for indoor navigation can be classified into three main groups namely map-based-navigation, map-building based navigation, and mapless-navigation. The first group is map-based- navigation where the AGV depends on a system based on a topological map of the environment or a user-created map by modelling the environment where the AGV will operate. The second group is map-building based navigation where the AGV is equipped with a system that constructs its own geometric or topological representation for its navigation and the third group employs mapless-navigation where the AGV uses no explicit representation about the operational environment. It makes use of the visual images cue to recognize objects in the surrounding to determine a collision-free navigation path. (DeSouza and Kak, 2002)
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Road Surface Obstacle Detection using Vision and LIDAR for Autonomous Vehicle

Road Surface Obstacle Detection using Vision and LIDAR for Autonomous Vehicle

Based on the results of the cascaded system, it can be seen that the average time is approximately equivalent to the sum of the processing time of the two individual systems. This is due to the alternate execution of each algorithm. For the pothole detection, the accuracy was decreased by 5.35%, while for the speed hump detection; the accuracy was increased by 0.10%. However, the increase in accuracy was due to the longer route taken. Taking a look at the number of false negative under the speed hump detection on the cascaded system, it can be seen that the number of missed speed hump is almost half the number of speed humps to be detected. Again, this is due to the alternate execution of each algorithm. Hence, it can be said that when the two systems are cascaded, the results are still reliable and accurate, however the number of potholes and speed humps missed increases. This observation on the system will be problematic as the number of potholes and speed humps to be detected increases. Thus, the system can be cascaded as long as the potholes and speed humps are minimal.
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Lane Keeping and Pedestrian Avoidance for a Vision-Based Autonomous Test Vehicle

Lane Keeping and Pedestrian Avoidance for a Vision-Based Autonomous Test Vehicle

The success of the pedestrian avoidance algorithm used in this project was found to be limited by the constraints of the Kinect’s hardware and machine learning al- gorithms. For the pedestrian avoidance algorithm developed for this thesis to be applicable in real world situations, the reliability of the Kinect’s human detection ca- pabilities would have to be improved for situations where the Kinect is in motion. If the Kinect were able to detect and track human bodies with the same 100% accuracy observed while the Kinect was non moving instead of the current 40% failure rate while in motion, then the pedestrian avoidance algorithms developed for this thesis could be used to increase the safety of product transport vehicles in warehouses, hospitals, and other facilities where mobile robots operate in prox- imity to humans. The principles for the pedestrian-avoidance algorithm developed in this thesis could also potentially be used for autonomous pedestrian avoidance in automobiles if the detection of distance, loop rate, and body tracking reliability were improved.
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A Vision based Vehicle Detection System

A Vision based Vehicle Detection System

Navigation systems are used in a wide range of applications such as: Automotive Navigations System, Marine Navigation System, Global Positioning System, Surgical Navigation System, Inertial Guidance system and Robotic mapping.in addition to navigation systems advance driver assistance system is there to maintain safe speed, driving within lane, collision avoidance and at last reducing the severity of accident. There are a wide range of issues and difficult tasks in the domain of navigation systems such as complex backgrounds, low-visibility, weather conditions, cast shadows, strong headlights, direct sunlight during dusk and dawn, uneven street illumination and the problem of occlusions on which this thesis is concentrating. In modest term 4 levels should be accomplished to make a robust navigation system: detection, localization, recognition and understanding. In this research work a number of experiments were carried out on different real world data sets for evaluation and analysis of the methods applied to obtain the best results for detecting vehicles. In the field of surveillance, autonomous navigation, scene understanding occlusion is one of the most common problem and due to this problem lots of vision based algorithms lacks in robustness.
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Natural landmark detection for visually-guided robot navigation

Natural landmark detection for visually-guided robot navigation

Abstract. The main difficulty to attain fully autonomous robot nav- igation outdoors is the fast detection of reliable visual references, and their subsequent characterization as landmarks for immediate and un- ambiguous recognition. Aimed at speed, our strategy has been to track salient regions along image streams by just performing on-line pixel sam- pling. Persistent regions are considered good candidates for landmarks, which are then characterized by a set of subregions with given color and normalized shape. They are stored in a database for posterior recogni- tion during the navigation process. Some experimental results showing landmark-based navigation of the legged robot Lauron III in an outdoor setting are provided.
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Vision based autonomous vehicle driving control system

Vision based autonomous vehicle driving control system

LITERATURE REVIEW Sensors Cameras Infrared Magnetic Radar Lane Detection Using Image Processing and Analysis Process Related Research Review Edge Detection Hough Transform Vehicle Modell[r]

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Vision-based Navigation Using Landmark Recognition for Unmanned Aerial Vehicles

Vision-based Navigation Using Landmark Recognition for Unmanned Aerial Vehicles

The thesis begins with a literature review (Chapter 2) that explores and outlines the current state-of-the-art methods used by both traditional and visual positioning systems for unmanned aerial vehicles. It proceeds to review current positioning methods and other sensors commonly available on unmanned vehicles. The review then describes and discusses methods for a higher-level visual navigation system, using feature description and matching methods based on work in other fields. The literature review has two aims: the first is to demonstrate that the current work in the field of visual positioning is focused on approaches distinct from the method proposed by this thesis. The second aim is to demonstrate that the algorithms surrounding the feature descriptor and matcher, such as landmark extraction and pose estimation, are well studied and that the data required, such as geographical reference databases and efficient retrieval methods, are available and accessible. This thus allows the thesis to concentrate on the core task: the recognition problem. Next, the System Overview chapter (Chapter 3) outlines the theory of operation and architecture of the proposed system. This includes a discussion of how the system operates and where it would fit among other systems onboard an autonomous vehicle. It also explains the proposed system architecture, including reasons behind the need for modularity and the various sub-systems that are required.
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Novel landmark guided routes in ants

Novel landmark guided routes in ants

A follow-up study (Fukushi and Wehner, 2004), only part of which we consider here, indicated that the interactions between path integration and landmark guidance may be more complex than simple inhibition of the performance of a home-vector. The ants’ homing behaviour when they had a normal home- vector was compared with their behaviour when there was none. As before, ants were taken from the feeder to one of several release sites. Two examples are shown in Fig.·3B. The normal route from the feeder (solid lines) is a straight trajectory as far as the edge of the terrace. The dotted lines are trajectories with a displaced start. The displaced trajectories begin straight but become convoluted towards the edge of the terrace – perhaps because the local view then looked wrong to the ants. Additionally, on some trials, ants were put in a zero home- vector state by allowing them to follow their normal route home and then catching them just before they reached the nest. When these ants are released, their path integration system indicates that they are already close to the nest. These ‘zero-vector’ ants also found their way to the nest, but with two intriguing
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Vision-based smoke detection

Vision-based smoke detection

Gray level variation in the horizon was used to measure the amount of fog in an image and then estimate the visibility distance in [52]. However, a model needs to be built for measuring distance by using a binocular camera, which means this method is not suitable for fog detection with single camera or classification of fog images. Two clues of fog presence were considered for fog detection in [14]. One is the reduced visibility distance, which was calculated from the camera projection equations; the other is the blurring effect due to fog, which was estimated by measuring the high frequency components in images. Gallen et al. proposed to detect the presence of fog in images captured by in-vehicle mul- tipurpose cameras during nighttime by means of two methods, which are based on the detection of the back-scattered veil induced by the vehicle ego lights and the detection of halos around light sources in the vehicle environment respectively [38]. To distinguish im- ages with fog present from those free of fog, Gabor filters at different frequencies, scales and orientations were adopted as global descriptors for characterizing images [121]. Liu and Lu proposed to calculate the characteristic parameters of the gray-scale histograms and use a series of thresholds to determine which fog level a test image belongs to [90]. A highly effective method for fog region of interest segmentation based on geodesic maps computation was proposed as well as a novel joint fog density and horizon line estimation process in [53].
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Vision Based Localization Algorithm Based on Landmark Matching, Triangulation, Reconstruction, and Comparison

Vision Based Localization Algorithm Based on Landmark Matching, Triangulation, Reconstruction, and Comparison

Feature-based localization algorithms are often simpler and more reliable, especially in dynamic environments, but the presence of nonunique landmarks is the serious concern. Fea- ture-based methods can be simpler due to the lack of a training phase. It is quite common to find multiple entities of similar objects, such as a set of dining chairs, an array of partitions, or a series of doors, in the case of indoor navigation. These nonunique natural landmarks cause serious data-association problems for many generic position-estimation algorithms [11]. While unique landmarks can be introduced by the place- ment of artificial objects, the preparation, maintenance, and environment-modification requirements make them unpop- ular. Robust estimation methods, notably RANSAC [12], can tolerate outliers, “poisoned points,” or nonunique feature matches, to a certain extent. RANSAC relies on repetitive random sampling. Its performance deteriorates rapidly with an increasing proportion of nonunique matches. Markov-chain Monte Carlo expectation-maximization (MCMCEM) [13] is a promising data-association technique, which demonstrates success in solving the 3-D scene model-estimation problem from a collection of image data. It assumes that all the 3-D features are visible from all the views. Although this is a common and very reasonable assumption for computer vision, occlusion is a crucial problem that should not be overlooked in robot navigation.
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Automated Guided Vehicle Using Line Following Detection

Automated Guided Vehicle Using Line Following Detection

This project is to making the automated guided vehicle (AGV) using line following detection. This AGV usually used in material transportation in an automated production line or in an automatic storage and retrieval system. This typical project consist of the hardware (the vehicle consist of mechanical device) while controlled by electronics part and some algorithm. For this project, we used the PIC to integrated the sensor and the motor as shown in figure 1.1

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Autonomous Learning for Detection of JavaScript Attacks: Vision or Reality?

Autonomous Learning for Detection of JavaScript Attacks: Vision or Reality?

While the static and dynamic detectors are nearly on par, the combination of both is significantly higher. In com- parison to the performance of the anti-virus tools, all de- tectors show a good detection performance. Especially the combined detector is able to classify approximately twice as much malicious URLs correctly. Surprisingly however, none of the considered features or learning methods attains a de- tection rate of more than 90%, as reported from previous work, which used manually sanitized datasets. To investi- gate this further we decide to create a subset of the malicious JavaScript code using the anti-virus tools as an additional sanitization instance. The assumption is that previous work always used manually sanitized datasets, so this way of au- tomatic sanitization is expected to result in a better detec- tion performance. In this new AV-Alerts dataset only those 890 URLs are included which both anti-virus tools raised an alert for. The results of the evaluation of our learning-based detection methods on this dataset are listed in Table 4.
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