Panoramic View-Based Navigation in Outdoor Environments Based on Support Vector Learning

Morita, Hiroshi; Hild, Michael; Miura, Jun; Shirai, Yoshiaki

doi:10.1109/iros.2006.282636

Cited by 23 publications

(11 citation statements)

References 15 publications

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“…Actually, vision has been used in numerous robotic applications to successfully achieve a task (e.g. obstacle avoidance for navigation [12], [13], [14], [15], human recognition for Human-Robot Interaction [16], [17], activity recognition for cooperative behaviour [18], [19], [20] and object identification for manipulation [21], [22], [23], to name only a few). However, despite significant achievements, the problem of detecting and recognising objects efficiently and accurately still remains a scientific challenge when real scenes are considered.…”

Section: Introductionmentioning

confidence: 99%

Object Detection and Recognition for Assistive Robots: Experimentation and Implementation

Martínez-Martín

Pobil

2017

IEEE Robot. Automat. Mag.

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Abstract-Technological advances are currently being directed to assist the human population in performing ordinary tasks in everyday settings. In this context, a key issue is the interaction with objects of varying size, shape and degree of mobility. Consequently, autonomous assistive robots must be provided with the ability to process visual data in real time so that they can react adequately for quickly adapting to changes in the environment. Reliable object detection and recognition is usually a necessary early step to achieve this goal. In spite of significant research achievements, this issue still remains a challenge when real-life scenarios are considered. In this paper, we present a vision system for assistive robots that is able to detect and recognise objects from a visual input in ordinary environments in real time. The system computes colour, motion and shape cues combining them in a probabilistic manner to accurately achieve object detection and recognition, taking some inspiration from vision science. In addition, with the purpose of processing the input visual data in real-time, a Graphical Processing Unit (GPU) has been employed. The presented approach has been implemented and evaluated on a humanoid robot torso located at realistic scenarios. For further experimental validation, a public image repository for object recognition has been used, allowing a quantitative comparison with respect to other state-of-the-art techniques when real-world scenes are considered. Finally, a temporal analysis of the performance is provided with respect to image resolution and number of target objects in the scene.

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Section: Introductionmentioning

confidence: 99%

Object Detection and Recognition for Assistive Robots: Experimentation and Implementation

Martínez-Martín

Pobil

2017

IEEE Robot. Automat. Mag.

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“…Thus, a visual input can provide information about the observed scenarios and the physical objects within it. Actually, a great series of robotic platforms use image processing to properly perform their tasks as navigation, for detecting and properly avoiding obstacles [5][6][7][8]; manipulation, for identifying objects to interact with [9,10]; cooperative behaviour, for identifying the activities of other team members [11][12][13]; or human-robot interaction, for properly recognizing the human being with who interacts [14,15].…”

Section: Introductionmentioning

confidence: 99%

Object Recognition in Cluttered Environments

Martínez-Martín

Pobil²

2013

Computer Graphics and Imaging / 798: Signal Processing, Pattern Recognition and Applications

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On the way to autonomous robots able to perceive all the surrounding space, this paper focuses on visual object recognition in real scenarios where those objects can be occluded, modify their appearance due to a viewpoint changed or different illumination conditions, etc. In this paper, we aim at robustly detecting and recognizing different objects in real-life scenarios from a visual input. For that, we present an appoach to properly build a peripersonal background model such that the target objects are always detected and recognized without any a-priori knowledge about the robotic system. Several objects and different scene conditions have been used to evaluate the method's performance by providing successful object detection and recognition in all the cases, as shown in the experimental section.

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“…Among various visual navigation approaches, view-based navigation has been shown to be effective [1], [2], [3], [4]. Its typical approach is as follows.…”

Section: Introductionmentioning

confidence: 99%

View sequence generation for view-based outdoor navigation

Kaneko

Miura

2011

The First Asian Conference on Pattern Recognition

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This paper describes a method of generating a new view sequence for view-based outdoor navigation. View-based navigation approaches have been shown to be effective but have a drawback that a view sequence for the route to be navigated is needed beforehand. This will be an issue especially for navigation in an open space where numerous potential routes exist; it is almost impossible to take view sequences for all of the routes by actually moving on them. We therefore develop a method of generating a view sequence for arbitrary routes from an omnidirectional view sequence taken at a limited movement. The method is based on visual odometry-based map generation and an image-to-image morphing using homography. The effectiveness of the method is validated by view-based localization experiments.

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Panoramic View-Based Navigation in Outdoor Environments Based on Support Vector Learning

Cited by 23 publications

References 15 publications

Object Detection and Recognition for Assistive Robots: Experimentation and Implementation

Object Detection and Recognition for Assistive Robots: Experimentation and Implementation

Object Recognition in Cluttered Environments

View sequence generation for view-based outdoor navigation

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