Vision for mobile robot navigation: a survey

DeSouza, Guilherme N.; Kak, Avinash C.

doi:10.1109/34.982903

Cited by 1,192 publications

(550 citation statements)

References 135 publications

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“…However, it still becomes a problem when real-time and high-precision performance is required. DeSouza and Kak (2002) presented an investigation of the developments in the fields related to vision for mobile robot navigation in the past 20 years. The differences in how vision is used for indoor and outdoor robots are large; thus they divided their investigation into two different categories: indoor navigation and outdoor navigation.…”

Section: Introductionmentioning

confidence: 99%

Stereo-Image Matching Using a Speeded Up Robust Feature Algorithm in an Integrated Vision Navigation System

et al. 2012

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Visual navigation is comparatively advanced without a Global Positioning System (GPS). It obtains environmental information via real-time processing of the data gained through visual sensors. Compared with other methods, visual navigation is a passive method that does not launch light or other radiation applications, thus making it easier to hide. The novel navigation system described in this paper uses stereo-matching combined with Inertial Measurement Units (IMU). This system applies photogrammetric theory and a matching algorithm to identify the matching points of two images of the same scene taken from different views and obtains their 3D coordinates. Integrated with the orientation information output by the IMU, the system reduces model-accumulated errors and improves the point accuracy.

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Section: Introductionmentioning

confidence: 99%

Stereo-Image Matching Using a Speeded Up Robust Feature Algorithm in an Integrated Vision Navigation System

et al. 2012

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“…We do not attempt to review vision for mobile robot guidance as an extensive review has recently been published [13].…”

Section: More Recent Computer Visionmentioning

confidence: 99%

Embodied categorisation for vision-guided mobile robots

Barnes

Liu

2004

Pattern Recognition

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This paper outlines a philosophical and psycho-physiological basis for embodied perception, and develops a framework for conceptual embodiment of vision-guided robots. We argue that categorisation is important in all stages of robot vision. Further, that classical computer vision is unsuitable for this categorisation, however, through conceptual embodiment, active perception can be e ective. We present a methodology for developing vision-guided robots that applies embodiment, explicitly and implicitly, in categorising visual data to facilitate e cient perception and action. Finally, we present systems developed using this methodology, and demonstrate that embodied categorisation can make algorithms more e cient and robust. Crown

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“…Computer vision in autonomous mobile robotics is a very well known topic that is being treated by many research groups [1]. However, the use of perception techniques to automatically learn and recognize the environment and the objects located on it is probably not so well known.…”

Section: Introductionmentioning

confidence: 99%

Robot Vision for Autonomous Object Learning and Tracking

Sanfeliu

2003

Lecture Notes in Computer Science

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Abstract. In this paper we present a summary of some of the research that we are developing in the Institute of Robotics of the CSIC-UPC, in the field of Learning and Robot Vision for autonomous mobile robots. We describe the problems that we have found and some solutions that have been applied in two issues: tracking objects and learning and recognition of 3D objects in robotic environments. We will explain some of the results accomplished.

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Vision for mobile robot navigation: a survey

Cited by 1,192 publications

References 135 publications

Stereo-Image Matching Using a Speeded Up Robust Feature Algorithm in an Integrated Vision Navigation System

Stereo-Image Matching Using a Speeded Up Robust Feature Algorithm in an Integrated Vision Navigation System

Embodied categorisation for vision-guided mobile robots

Robot Vision for Autonomous Object Learning and Tracking

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