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13th IEEE International Symposium on Computational Intelligence and Informatics (CINTI)

作者： Karan, Branko Inst Tech Sci Serbian Acad Sci & Arts Belgrade Serbia

ISBN: (纸本)9781467352062;9781467352055

Visual tracking of stationary points positioned at a large distance from moving robot provides a good basis for determining robot orientation and it may speed-up operation of structure from motion and navigation algorithms. This paper considers an implementation of the tracker that builds a far background model by assigning each visually tracked point a degree of membership that express an extent to which an apparent motion of corresponding image patch is in accordance to motion of projections of points at infinity. An experimental implementation of the tracker is described together with illustrative examples of its operation.

关键词： robot vision vision-based robot navigation background extraction

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Towards implementation of far background tracker for vision-based robot navigation

Towards implementation of far background tracker for vision-...

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International Symposium on Computational Intelligence and Informatics

作者： Branko Karan Institute of Technical Sciences Serbian Academy of Sciences and Arts Belgrade Serbia

ISBN: (纸本)9781467352055

关键词： robot vision vision-based robot navigation background extraction robot vision Mobile robot navigation image patch Mobile robots visual tracking Surgery BACKGROUND Visual servoing

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Convolutional Neural Network-based robot navigation Using Uncalibrated Spherical Images

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SENSORS 2017年第6期17卷 1341-1341页

作者： Ran, Lingyan Zhang, Yanning Zhang, Qilin Yang, Tao Northwestern Polytech Univ Sch Comp Sci & Engn Xian 710072 Peoples R China HERE Technol Automot Div Highly Automated Driving Team Chicago IL 60606 USA

vision-based mobile robot navigation is a vibrant area of research with numerous algorithms having been developed, the vast majority of which either belong to the scene-oriented simultaneous localization and mapping (SLAM) or fall into the category of robot-oriented lane-detection/trajectory tracking. These methods suffer from high computational cost and require stringent labelling and calibration efforts. To address these challenges, this paper proposes a lightweight robot navigation framework based purely on uncalibrated spherical images. To simplify the orientation estimation, path prediction and improve computational efficiency, the navigation problem is decomposed into a series of classification tasks. To mitigate the adverse effects of insufficient negative samples in the "navigation via classification" task, we introduce the spherical camera for scene capturing, which enables 360 degrees fisheye panorama as training samples and generation of sufficient positive and negative heading directions. The classification is implemented as an end-to-end Convolutional Neural Network (CNN), trained on our proposed Spherical-Navi image dataset, whose category labels can be efficiently collected. This CNN is capable of predicting potential path directions with high confidence levels based on a single, uncalibrated spherical image. Experimental results demonstrate that the proposed framework outperforms competing ones in realistic applications.

关键词： convolutional neural networks vision-based robot navigation spherical camera navigation via learning

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CAT-SLAM: probabilistic localisation and mapping using a continuous appearance-based trajectory

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INTERNATIONAL JOURNAL OF robotICS RESEARCH 2012年第4期31卷 429-451页

作者： Maddern, Will Milford, Michael Wyeth, Gordon Queensland Univ Technol Sch Elect Engn & Comp Sci Brisbane Qld 4000 Australia

This paper describes a new system, dubbed Continuous Appearance-based Trajectory Simultaneous Localisation and Mapping (CAT-SLAM), which augments sequential appearance-based place recognition with local metric pose filtering to improve the frequency and reliability of appearance-based loop closure. As in other approaches to appearance-based mapping, loop closure is performed without calculating global feature geometry or performing 3D map construction. Loop-closure filtering uses a probabilistic distribution of possible loop closures along the robot's previous trajectory, which is represented by a linked list of previously visited locations linked by odometric information. Sequential appearance-based place recognition and local metric pose filtering are evaluated simultaneously using a Rao-Blackwellised particle filter, which weights particles based on appearance matching over sequential frames and the similarity of robot motion along the trajectory. The particle filter explicitly models both the likelihood of revisiting previous locations and exploring new locations. A modified resampling scheme counters particle deprivation and allows loop-closure updates to be performed in constant time for a given environment. We compare the performance of CAT-SLAM with FAB-MAP (a state-of-the-art appearance-only SLAM algorithm) using multiple real-world datasets, demonstrating an increase in the number of correct loop closures detected by CAT-SLAM.

关键词： appearance-based SLAM vision-based robot navigation CAT-SLAM

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Landmark Manipulation System for Mobile robot navigation

Landmark Manipulation System for Mobile Robot Navigation

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6th International Conference on Computer Engineering and Systems (ICCES)

作者： Elmogy, Mohammed Mansoura Univ Fac Comp & Informat Sci Dept Informat Syst Mansoura Egypt

ISBN: (纸本)9781424470426

In mobile robot scenarios, it is expected that the robot autonomously navigates through home or office environments and processes objects/landmarks during navigation. Landmark manipulation is identified as one important research area in robot navigation systems. We have developed an online robot landmark processing system (RLPS) to detect, classify, and localize different types of landmarks during robot navigation. The RLPS is based on a two-step classification stage which is robust and invariant towards scaling and translations. It provides a good balance between fast processing time and high detection accuracy by combining the strengths of appearance-based and model-based object classification techniques. The experimental results showed that the RLPS is more powerful as it recognizes a wide range of landmarks and efficiently handles landmarks with occlusions, viewpoint variances, and illumination changes.

关键词： robot vision object detection and classification vision-based robot navigation

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robot homing by exploiting panoramic vision

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AUTONOMOUS robotS 2005年第1期19卷 7-25页

作者： Argyros, AA Bekris, KE Orphanoudakis, SC Kavraki, LE Fdn Res & Technol Hellas FORTH Inst Comp Sci Iraklion Crete Greece Rice Univ Dept Comp Sci Houston TX 77251 USA

We propose a novel, vision-based method for robot homing, the problem of computing a route so that a robot can return to its initial "home" position after the execution of an arbitrary "prior" path. The method assumes that the robot tracks visual features in panoramic views of the environment that it acquires as it moves. By exploiting only angular information regarding the tracked features, a local control strategy moves the robot between two positions, provided that there are at least three features that can be matched in the panoramas acquired at these positions. The strategy is successful when certain geometric constraints on the configuration of the two positions relative to the features are fulfilled. In order to achieve long-range homing, the features' trajectories are organized in a visual memory during the execution of the "prior" path. When homing is initiated, the robot selects Milestone Positions ( MPs) on the "prior" path by exploiting information in its visual memory. The MP selection process aims at picking positions that guarantee the success of the local control strategy between two consecutive MPs. The sequential visit of successive MPs successfully guides the robot even if the visual context in the "home" position is radically different from the visual context at the position where homing was initiated. Experimental results from a prototype implementation of the method demonstrate that homing can be achieved with high accuracy, independent of the distance traveled by the robot. The contribution of this work is that it shows how a complex navigational task such as homing can be accomplished efficiently, robustly and in real-time by exploiting primitive visual cues. Such cues carry implicit information regarding the 3D structure of the environment. Thus, the computation of explicit range information and the existence of a geometric map are not required.

关键词： robot homing omni-directional vision panoramic cameras vision-based robot navigation

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