Dynamic omnidirectional vision for mobile robots

1986 ◽  
Vol 3 (1) ◽  
pp. 5-17 ◽  
Author(s):  
Zuo L. Cao ◽  
Sung J. Oh ◽  
Ernest L. Hall
Keyword(s):  
Mobile Robots ◽  
Omnidirectional Vision

Localization of mobile robots with omnidirectional vision using Particle Filter and iterative SIFT

2006 ◽  
Vol 54 (9) ◽  
pp. 758-765 ◽  
Author(s):  
Hashem Tamimi ◽  
Henrik Andreasson ◽  
André Treptow ◽  
Tom Duckett ◽  
Andreas Zell
Keyword(s):  
Mobile Robots ◽  
Particle Filter ◽  
Omnidirectional Vision

Estimating Position of Mobile Robots From Omnidirectional Vision Using an Adaptive Algorithm

2015 ◽  
Vol 45 (8) ◽  
pp. 1633-1646 ◽  
Author(s):  
Luyang Li ◽  
Yun-Hui Liu ◽  
Kai Wang ◽  
Mu Fang
Keyword(s):  
Mobile Robots ◽  
Adaptive Algorithm ◽  
Omnidirectional Vision

scholarly journals Method for SLAM Based on Omnidirectional Vision: A Delayed-EKF Approach

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Rodrigo Munguía ◽  
Carlos López-Franco ◽  
Emmanuel Nuño ◽  
Adriana López-Franco
Keyword(s):  
Mobile Robot ◽  
Mobile Robots ◽  
Autonomous Navigation ◽  
Real Data ◽  
Omnidirectional Vision ◽  
Wide Field ◽  
Visual Sensor ◽  
Cluttered Environments ◽  
Localization And Mapping ◽  
Wide Field Of View

This work presents a method for implementing a visual-based simultaneous localization and mapping (SLAM) system using omnidirectional vision data, with application to autonomous mobile robots. In SLAM, a mobile robot operates in an unknown environment using only on-board sensors to simultaneously build a map of its surroundings, which it uses to track its position. The SLAM is perhaps one of the most fundamental problems to solve in robotics to build mobile robots truly autonomous. The visual sensor used in this work is an omnidirectional vision sensor; this sensor provides a wide field of view which is advantageous in a mobile robot in an autonomous navigation task. Since the visual sensor used in this work is monocular, a method to recover the depth of the features is required. To estimate the unknown depth we propose a novel stochastic triangulation technique. The system proposed in this work can be applied to indoor or cluttered environments for performing visual-based navigation when GPS signal is not available. Experiments with synthetic and real data are presented in order to validate the proposal.

scholarly journals An FPGA-Based Omnidirectional Vision Sensor for Motion Detection on Mobile Robots

2012 ◽  
Vol 2012 ◽  
pp. 1-16 ◽  
Author(s):  
Jones Y. Mori ◽  
Janier Arias-Garcia ◽  
Camilo Sánchez-Ferreira ◽  
Daniel M. Muñoz ◽  
Carlos H. Llanos ◽  
...  
Keyword(s):  
Image Processing ◽  
Mobile Robots ◽  
Background Subtraction ◽  
Vision System ◽  
Low Cost ◽  
Center Of Mass ◽  
Omnidirectional Vision ◽  
Embedded Microprocessor ◽  
Xpc Target ◽  
Convex Mirror

This work presents the development of an integrated hardware/software sensor system for moving object detection and distance calculation, based on background subtraction algorithm. The sensor comprises a catadioptric system composed by a camera and a convex mirror that reflects the environment to the camera from all directions, obtaining a panoramic view. The sensor is used as an omnidirectional vision system, allowing for localization and navigation tasks of mobile robots. Several image processing operations such as filtering, segmentation and morphology have been included in the processing architecture. For achieving distance measurement, an algorithm to determine the center of mass of a detected object was implemented. The overall architecture has been mapped onto a commercial low-cost FPGA device, using a hardware/software co-design approach, which comprises a Nios II embedded microprocessor and specific image processing blocks, which have been implemented in hardware. The background subtraction algorithm was also used to calibrate the system, allowing for accurate results. Synthesis results show that the system can achieve a throughput of 26.6 processed frames per second and the performance analysis pointed out that the overall architecture achieves a speedup factor of 13.78 in comparison with a PC-based solution running on the real-time operating system xPC Target.

scholarly journals Calibration and three-dimensional reconstruction with a photorealistic simulator based on the omnidirectional vision system

2021 ◽  
Vol 18 (6) ◽  
pp. 172988142110593
Author(s):  
Ivan Kholodilin ◽  
Yuan Li ◽  
Qinglin Wang ◽  
Paul David Bourke
Keyword(s):  
Mobile Robots ◽  
Indoor Environment ◽  
Vision System ◽  
Three Dimensional ◽  
Ground Truth ◽  
Training Data ◽  
Omnidirectional Vision ◽  
Three Dimensional Reconstruction ◽  
Practical Applications ◽  
Dimensional Reconstruction

Recent advancements in deep learning require a large amount of the annotated training data containing various terms and conditions of the environment. Thus, developing and testing algorithms for the navigation of mobile robots can be expensive and time-consuming. Motivated by the aforementioned problems, this article presents a photorealistic simulator for the computer vision community working with omnidirectional vision systems. Built using unity, the simulator integrates sensors, mobile robots, and elements of the indoor environment and allows one to generate synthetic photorealistic data sets with automatic ground truth annotations. With the aid of the proposed simulator, two practical applications are studied, namely extrinsic calibration of the vision system and three-dimensional reconstruction of the indoor environment. For the proposed calibration and reconstruction techniques, the processes themselves are simple, robust, and accurate. Proposed methods are evaluated experimentally with data generated by the simulator. The proposed simulator and supporting materials are available online: http://www.ilabit.org .

scholarly journals A State-of-the-Art Review on Mapping and Localization of Mobile Robots Using Omnidirectional Vision Sensors

2017 ◽  
Vol 2017 ◽  
pp. 1-20 ◽  
Author(s):  
L. Payá ◽  
A. Gil ◽  
O. Reinoso
Keyword(s):  
Computer Vision ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Autonomous Vehicles ◽  
Scientific Community ◽  
Mobile Robotics ◽  
State Of The Art ◽  
Omnidirectional Vision ◽  
Vision Sensors ◽  
Source Of Information

Nowadays, the field of mobile robotics is experiencing a quick evolution, and a variety of autonomous vehicles is available to solve different tasks. The advances in computer vision have led to a substantial increase in the use of cameras as the main sensors in mobile robots. They can be used as the only source of information or in combination with other sensors such as odometry or laser. Among vision systems, omnidirectional sensors stand out due to the richness of the information they provide the robot with, and an increasing number of works about them have been published over the last few years, leading to a wide variety of frameworks. In this review, some of the most important works are analysed. One of the key problems the scientific community is addressing currently is the improvement of the autonomy of mobile robots. To this end, building robust models of the environment and solving the localization and navigation problems are three important abilities that any mobile robot must have. Taking it into account, the review concentrates on these problems; how researchers have addressed them by means of omnidirectional vision; the main frameworks they have proposed; and how they have evolved in recent years.

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