Position Estimation and Local Mapping Using Omnidirectional Images and Global Appearance Descriptors

Berenguer, Yerai; Payá, Luis

doi:10.3390/s151026368

Cited by 20 publications

(22 citation statements)

References 21 publications

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“…Several works have demonstrated the validity of these techniques in robot mapping and localization when the movement of the robot is restricted to the ground plane. For example, in [23], different 2D localization and mapping tasks have been carried out using global appearance descriptors, and they have been compared with some descriptors based on landmark extraction to compare the effectiveness and the computational cost. Ranganathan et al [28] presented a probabilistic topological mapping method that uses information of panoramic scenes captured by a ring of cameras mounted on the robot, and they are described using Fourier signature.…”

Section: State-of-the-art On Altitude Estimation and Global Appearancmentioning

confidence: 99%

“…We also consider that the map of the environment was constructed from a set of images captured while the robot had a planar movement and using global-appearance techniques. Previous works have shown that it is possible to estimate the pose (position and orientation) of the robot in this plane using this kind of technique [23]. In this work, Berenguer et al used a set of omnidirectional images captured from different poses in the ground plane (reference images) and obtained a holistic descriptor per image, using a combination of the Radon transform and gist.…”

Section: Introductionmentioning

confidence: 99%

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Relative Altitude Estimation Using Omnidirectional Imaging and Holistic Descriptors

et al. 2019

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Currently, many tasks can be carried out using mobile robots. These robots must be able to estimate their position in the environment to plan their actions correctly. Omnidirectional vision sensors constitute a robust choice to solve this problem, since they provide the robot with complete information from the environment where it moves. The use of global appearance or holistic methods along with omnidirectional images constitutes a robust approach to estimate the robot position when its movement is restricted to the ground plane. However, in some applications, the robot changes its altitude with respect to this plane, and this altitude must be estimated. This work focuses on this problem. A method based on the use of holistic descriptors is proposed to estimate the relative altitude of the robot when it moves upwards or downwards. This descriptor is constructed from the Radon transform of omnidirectional images captured by a catadioptric vision system. To estimate the altitude, the descriptor of the image captured from the current position is compared with the descriptor of the reference image, previously built. The framework is based on the use of phase correlation to calculate relative orientation and a method based on the compression-expansion of the columns of the holistic descriptor to estimate relative height. Only an omnidirectional vision sensor and image processing techniques are used to solve these problems. This approach has been tested using different sets of images captured both indoors and outdoors under realistic working conditions. The experimental results prove the validity of the method even in the presence of noise or occlusions.

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Section: State-of-the-art On Altitude Estimation and Global Appearancmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Relative Altitude Estimation Using Omnidirectional Imaging and Holistic Descriptors

et al. 2019

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“…They make use of the properties that this transform presents when it is used with panoramic images, and they also propose a Monte Carlo algorithm for robust localization. In a similar way, Berenguer et al [136] propose two methods to estimate the position of the robot by means of the omnidirectional image captured. On the one hand, the first method represents the environment through a sequence of omnidirectional images, and the Radon transform is used to describe the global appearance of the scenes.…”

Section: Mapless Navigation Systemsmentioning

confidence: 99%

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

2017

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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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“…Third, hybrid maps try to gather the advantages of the two previous approaches. They arrange the information into several layers with different levels of detail, containing topological models in the top layers that permit a rough localization and metric models in the bottom layers to refine this localization [21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Development of Height Indicators using Omnidirectional Images and Global Appearance Descriptors

et al. 2017

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Nowadays, mobile robots have become a useful tool that permits solving a wide range of applications. Their importance lies in their ability to move autonomously through unknown environments and to adapt to changing conditions. To this end, the robot must be able to build a model of the environment and to estimate its position using the information captured by the different sensors it may be equipped with. Omnidirectional vision sensors have become a robust option thanks to the richness of the data they capture. These data must be analysed to extract relevant information that permits estimating the position of the robot taking into account the number of degrees of freedom it has. In this work, several methods to estimate the relative height of a mobile robot are proposed and evaluated. The framework we present is based on the global appearance of the scenes, which has emerged as an efficient and robust alternative comparing to methods based on local features. All the algorithms have been tested with some sets of images captured under real working conditions in several indoor and outdoor spaces. The results prove that global appearance descriptors provide a feasible alternative to estimate topologically the relative altitude of the robot.

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Position Estimation and Local Mapping Using Omnidirectional Images and Global Appearance Descriptors

Cited by 20 publications

References 21 publications

Relative Altitude Estimation Using Omnidirectional Imaging and Holistic Descriptors

Relative Altitude Estimation Using Omnidirectional Imaging and Holistic Descriptors

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

Development of Height Indicators using Omnidirectional Images and Global Appearance Descriptors

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