Single Omnidirectional Camera Research Articles

The flow of baseline estimation using a single omnidirectional camera

Baseline is a distance between two cameras, but we cannot get information from a single camera. Baseline is one of the important parameters to find the depth of objects in stereo image triangulation. The flow of baseline is produced by moving the camera in horizontal axis from its original location. Using baseline estimation, we can determined the depth of an object by using only an omnidirectional camera. This research focus on determining the flow of baseline before calculating the disparity map. To estimate the flow and to tracking the object, we use three and four points in the surface of an object from two different data (panoramic image) that were already chosen. By moving the camera horizontally, we get the tracks of them. The obtained tracks are visually similar. Each track represent the coordinate of each tracking point. Two of four tracks have a graphical representation similar to second order polynomial.

Information-based view initialization in visual SLAM with a single omnidirectional camera

This paper presents a novel mechanism to initiate new views within the map building process for an EKF-based visual SLAM (Simultaneous Localization and Mapping) approach using omnidirectional images. In presence of non-linearities, the EKF is very likely to compromise the final estimation. Particularly, the omnidirectional observation model induces non-linear errors, thus it becomes a potential source of uncertainty. To deal with this issue we propose a novel mechanism for view initialization which accounts for information gain and losses more efficiently. The main outcome of this contribution is the reduction of the map uncertainty and thus the higher consistency of the final estimation. Its basis relies on a Gaussian Process to infer an information distribution model from sensor data. This model represents feature points existence probabilities and their information content analysis leads to the proposed view initialization scheme. To demonstrate the suitability and effectiveness of the approach we present a series of real data experiments conducted with a robot equipped with a camera sensor and map model solely based on omnidirectional views. The results reveal a beneficial reduction on the uncertainty but also on the error in the pose and the map estimate.

Investigation of Matlab® as platform in navigation and control of an Automatic Guided Vehicle utilising an omnivision sensor.

Automatic Guided Vehicles (AGVs) are navigated utilising multiple types of sensors for detecting the environment. In this investigation such sensors are replaced and/or minimized by the use of a single omnidirectional camera picture stream. An area of interest is extracted, and by using image processing the vehicle is navigated on a set path. Reconfigurability is added to the route layout by signs incorporated in the navigation process. The result is the possible manipulation of a number of AGVs, each on its own designated colour-signed path. This route is reconfigurable by the operator with no programming alteration or intervention. A low resolution camera and a Matlab® software development platform are utilised. The use of Matlab® lends itself to speedy evaluation and implementation of image processing options on the AGV, but its functioning in such an environment needs to be assessed.

A modified stochastic gradient descent algorithm for view-based SLAM using omnidirectional images

This paper describes an approach to the problem of Simultaneous Localization and Mapping (SLAM) based on Stochastic Gradient Descent (SGD) and using omnidirectional images. In the field of mobile robot applications, SGD techniques have never been evaluated with information gathered by visual sensors. This work proposes a SGD algorithm within a SLAM system which makes use of the beneficial characteristics of a single omnidirectional camera. The nature of the sensor has led to a modified version of the standard SGD to adapt it to omnidirectional geometry. Besides, the angular unscaled observation measurement needs to be considered. This upgraded SGD approach minimizes the non-linear effects which impair and compromise the convergence of traditional estimators. Moreover, we suggest a strategy to improve the convergence speed of the SLAM solution, which inputs several constraints in the SGD algorithm simultaneously, in contrast to former SGD approaches, which process only constraint independently. In particular, we focus on an efficient map model, established by a reduced set of image views. We present a series of experiments obtained with both simulated and real data. We validate the new SGD approach, compare the efficiency versus a standard SGD and demonstrate the suitability and the reliability of the approach to support real applications.

A Case Study on Graphic Path Analysis Using Various Mapping Techniques

This work offers a method for the visual-based navigation of a mobile robot in indoor environments, using a single omni-directional camera. The geometry of the catadioptric sensor and the method used to obtain a bird's eye (orthographic) view of the ground plane are presented. This picture evocatively abridges the solution to steering problems, by eliminating any perspective effects. The nature of each navigation task is taken hooked on account when conniving the compulsory navigation skills and environmental representations. Topological Navigation is cast-off for itinerant long distances plus does not require knowledge of the exact location of the robot but rather, a qualitative position happening the topological map. For indoor robots in structured environments, we consume owed separately with the cases of geometrical and topological models of interplanetary. For shapeless environments, we consume discussed the cases of navigation through optical activities, using methods from the appearance-based paradigm, and by recognition of specific substances in the environment. The navigation process cartels appearance based approaches and visual serving upon some environmental features. Graphic Path Succeeding is obligatory for native, actual precise navigation, for e.g. door traversal, docking. The robot is controlled to follow a pre-specified trail accurately, by tracking visual milestones in bird's eye views of the ground plane. By clearly separating the nature of these triangulation tasks, a simple besides yet powerful navigation scheme is obtained.

Visual Odometry through Appearance- and Feature-Based Method with Omnidirectional Images

In the field of mobile autonomous robots, visual odometry entails the retrieval of a motion transformation between two consecutive poses of the robot by means of a camera sensor solely. A visual odometry provides an essential information for trajectory estimation in problems such as Localization and SLAM (Simultaneous Localization and Mapping). In this work we present a motion estimation based on a single omnidirectional camera. We exploited the maximized horizontal field of view provided by this camera, which allows us to encode large scene information into the same image. The estimation of the motion transformation between two poses is incrementally computed, since only the processing of two consecutive omnidirectional images is required. Particularly, we exploited the versatility of the information gathered by omnidirectional images to perform both an appearance-based and a feature-based method to obtain visual odometry results. We carried out a set of experiments in real indoor environments to test the validity and suitability of both methods. The data used in the experiments consists of a large sets of omnidirectional images captured along the robot's trajectory in three different real scenarios. Experimental results demonstrate the accuracy of the estimations and the capability of both methods to work in real-time.

A nonparametric learning approach to range sensing from omnidirectional vision

We present a novel approach to estimating depth from single omnidirectional camera images by learning the relationship between visual features and range measurements available during a training phase. Our model not only yields the most likely distance to obstacles in all directions, but also the predictive uncertainties for these estimates. This information can be utilized by a mobile robot to build an occupancy grid map of the environment or to avoid obstacles during exploration—tasks that typically require dedicated proximity sensors such as laser range finders or sonars. We show in this paper how an omnidirectional camera can be used as an alternative to such range sensors. As the learning engine, we apply Gaussian processes, a nonparametric approach to function regression, as well as a recently developed extension for dealing with input-dependent noise. In practical experiments carried out in different indoor environments with a mobile robot equipped with an omnidirectional camera system, we demonstrate that our system is able to estimate range with an accuracy comparable to that of dedicated sensors based on sonar or infrared light.

1A1-F14 特徴点フローモデルを用いた全方位カメラの位置姿勢推定による3次元環境モデリング

Map information is important for path planning and self-localization when mobile robots accomplish autonomous tasks. In unknown environments, mobile robots should generate an environment map by themselves. Then, we propose a method for construction of the 3D environment model by a mobile robot. The proposed method is based on structure from motion. We use a single omni-directional camera to measure the objects in the environment. The propose method estimates the camera motion with a feature flow model. The feature flow model makes it possible to estimate the camera motion precisely by taking advantage of the wide field of view of the omni-directional camera. Experimental results showed the effectiveness of the proposed method.

Appearance-Guided Monocular Omnidirectional Visual Odometry for Outdoor Ground Vehicles

In this paper, we describe a real-time algorithm for computing the ego-motion of a vehicle relative to the road. The algorithm uses as input only those images provided by a single omnidirectional camera mounted on the roof of the vehicle. The front ends of the system are two different trackers. The first one is a homography-based tracker that detects and matches robust scale-invariant features that most likely belong to the ground plane. The second one uses an appearance-based approach and gives high-resolution estimates of the rotation of the vehicle. This planar pose estimation method has been successfully applied to videos from an automotive platform. We give an example of camera trajectory estimated purely from omnidirectional images over a distance of 400 m. For performance evaluation, the estimated path is superimposed onto a satellite image. In the end, we use image mosaicing to obtain a textured 2-D reconstruction of the estimated path.

Tracking head positions in three-dimensional space by using a single omnidirectional camera

We propose a novel method using a single catadioptric omnidirectional camera to track 3-D head positions in nonintrusive biometric systems. Existing methods have not been able to deal with cases in which the ground plane is nonorthogonal to the optical axis of the camera. To overcome this problem, our proposed method presents the following three improvements: (i) By using 1-D tracking of the feet and head positions combined with a circular constraint in images instead of conventional 2-D tracking methods, the accuracy and computational efficiency of the tracking process is significantly improved, (ii) based on the detected 2-D head position in images with the precalibration information of the camera, the 3-D head positions can be obtained, and (iii) the 3-D head positions can be obtained without having the optical axis of the camera orthogonal to the ground plane. This makes the proposed method feasible in practical environments that use omnidirectional camera systems. Experimental results showed that the proposed method was able to track 3-D head positions at real-time speed.

전방위 영상을 이용한 이동 로봇의 전역 위치 인식

This paper presents a global localization method using circular correlation of an omni-directional image. The localization of a mobile robot, especially in indoor conditions, is a key component in the development of useful service robots. Though stereo vision is widely used for localization, its performance is limited due to computational complexity and its narrow view angle. To compensate for these shortcomings, we utilize a single omni-directional camera which can capture instantaneous 360° panoramic images around a robot. Nodes around a robot are extracted by the correlation coefficients of CHL (Circular Horizontal Line) between the landmark and the current captured image. After finding possible near nodes, the robot moves to the nearest node based on the correlation values and the positions of these nodes. To accelerate computation, correlation values are calculated based on Fast Fourier Transforms. Experimental results and performance in a real home environment have shown the feasibility of the method.

Range determination for mobile robots using an omnidirectional camera

We propose a method for computing the absolute distances to static obstacles using a single omnidirectional camera. The method is applied to mobile robots. We achieve this without restricting the application to predetermined translations or the use o

Lane Tracking with Omnidirectional Cameras: Algorithms and Evaluation

With a panoramic view of the scene, a single omnidirectional camera can monitor the 360-degree surround of the vehicle or monitor the interior and exterior of the vehicle at the same time. We investigate problems associated with integrating driver assistance functionalities that have been designed for rectilinear cameras with a single omnidirectional camera instead. Specifically, omnidirectional cameras have been shown effective in determining head gaze orientation from within a vehicle. We examine the issues involved in integrating lane tracking functions using the same omnidirectional camera, which provide a view of both the driver and the road ahead of the vehicle. We present analysis on the impact of the omnidirectional camera's reduced image resolution on lane tracking accuracy, as a consequence of gaining the expansive view. And to do so, we present Omni-VioLET, a modified implementation of the vision-based lane estimation and tracking system (VioLET), and conduct a systematic performance evaluation of both lane-trackers operating on monocular rectilinear images and omnidirectional images. We are able to show a performance comparison of the lane tracking from Omni-VioLET and Recti-VioLET with ground truth using images captured along the same freeway road in a specified course. The results are surprising: with 1/10th the number of pixels representing the same space and about 1/3rd the horizontal image resolution as a rectilinear image of the same road, the omnidirectional camera implementation results in only three times the amount the mean absolute error in tracking the left lane boundary position.

Range determination for mobile robots using an omnidirectional camera

We propose a method for computing the absolute distances to static obstacles using a single omnidirectional camera. The method is applied to mobile robots. We achieve this without restricting the a...

Mobile robot localization based on omnicamera

In this paper a method of mobile robot localization in an unknown indoor environment is presented. The robot is equipped with a single omni-directional camera. The images are transformed into polar representation. Based on the images and their polar coordinates, the natural landmarks of the environment are extracted. We recognize the following features of the environment-vertical and horizontal lines and areas of unique colour. By using images taken from the omni-camera wall plane landmarks are extracted. With a 360 degree view of the robot's environment, the robot's position can easily be estimated and this information can be used to follow its path. The method was tested with the use of mobile robot Navigator 1 in a real office environment

A Framework and Architecture for Multi-Robot Coordination

In this paper, we present a framework and the software architecture for the deployment of multiple autonomous robots in an unstructured and unknown environment, with applications ranging from scouting and reconnaissance, to search and rescue, to manipulation tasks, to cooperative localization and mapping, and formation control. Our software framework allows a modular and hierarchical approach to programming deliberative and reactive behaviors in autonomous operation. Formal definitions for sequential composition, hierarchical composition, and parallel composition allow the bottom-up development of complex software systems. We demonstrate the algorithms and software on an experimental testbed that involves a group of carlike robots, each using a single omnidirectional camera as a sensor without explicit use of odometry.

A Framework and Architecture for Multi-Robot Coordination

In this paper, we present a framework and the software architecture for the deployment of multiple autonomous robots in an unstructured and unknown environment, with applications ranging from scouting and reconnaissance, to search and rescue, to manipulation tasks, to cooperative localization and mapping, and formation control. Our software framework allows a modular and hierarchical approach to programming deliberative and reactive behaviors in autonomous operation. Formal definitions for sequential composition, hierarchical composition, and parallel composition allow the bottom-up development of complex software systems. We demonstrate the algorithms and software on an experimental testbed that involves a group of carlike robots, each using a single omnidirectional camera as a sensor without explicit use of odometry.

3D scene reconstruction from cylindrical panoramic images

In this paper, we address the problem of recovering 3D scene structure from omni-directional cylindrical panoramic images. In our application, the images are obtained from a single omni-directional camera mounted on top of a mobile robot. Depth can be estimated from a single pair of images by a stereo vision technique. However, depth cannot be estimated reliably in the robot’s heading direction. Moreover, depth estimates obtained from a single pair of images tend to be noisy. To overcome these problems we have developed a multi-baseline stereo vision method which uses more than two images obtained from multiple non-co-linear viewpoints. Depth data obtained from different pairs of images is fused in a probabilistic framework. We present our method and experimental results obtained using image data acquired by our robot. The results indicate that robust and reliable depth estimates can be obtained using the proposed method.

Vision-based navigation and environmental representations with an omnidirectional camera

Proposes a method for the visual-based navigation of a mobile robot in indoor environments, using a single omnidirectional (catadioptric) camera. The geometry of the catadioptric sensor and the method used to obtain a bird's eye (orthographic) view of the ground plane are presented. This representation significantly simplifies the solution to navigation problems, by eliminating any perspective effects. The nature of each navigation task is taken into account when designing the required navigation skills and environmental representations. We propose two main navigation modalities: topological navigation and visual path following. Topological navigation is used for traveling long distances and does not require knowledge of the exact position of the robot but rather, a qualitative position on the topological map. The navigation process combines appearance based methods and visual servoing upon some environmental features. Visual path following is required for local, very precise navigation, e.g., door traversal, docking. The robot is controlled to follow a prespecified path accurately, by tracking visual landmarks in bird's eye views of the ground plane. By clearly separating the nature of these navigation tasks, a simple and yet powerful navigation system is obtained.