Autonomous visual self-localization in completely unknown environment

Sadeghi‐Tehran, Pouria; Behera, Sasmita; Angelov, Plamen; Andréu, Javier

doi:10.1109/eais.2012.6232811

Cited by 4 publications

(5 citation statements)

References 21 publications

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“…Alternatively, in this work, we propose a light-weight, and memory efficient approach for cooperative mapping, which would be more appropriate for autonomous UAVs, without requiring expensive sensors and costly algorithms. Our main contribution is to show that we can use an evolving recursive algorithm for landmark identification and map aggregation, building upon the Recursive Density Estimation (RDE) approach [10], [11].…”

Section: Related Workmentioning

confidence: 99%

“…We developed our technique for landmark detection by extending our previous work [10], [11], where Recursive Density Estimation (RDE) was introduced for automatically detecting landmarks from front-view cameras. The only inputs for RDE are the stream of images from a camera, no pretraining and no human involvement is required.…”

Section: A Automatic Landmark Detectionmentioning

confidence: 99%

“…Hence, in this paper, we propose a light-weight, memoryefficient, evolving approach for landmark identification and map aggregation, based on the Recursive Density Estimation (RDE) [10], [11] method. We also demonstrate how this approach can be applied when estimating landmarks in real-time by real UAVs.…”

Section: Introductionmentioning

confidence: 99%

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Towards Evolving Cooperative Mapping for Large-Scale UAV Teams

Yourdshahi

Angelov

Marcolino

et al. 2018

2018 IEEE Symposium Series on Computational Intelligence (SSCI)

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A team of UAVs has great potential to handle real-world challenges. Knowing the environment is essential to perform in an effective manner. However, in many situations, a map of the environment will not be available. Additionally, for autonomous systems, it is necessary to have approaches that require little energy, computing, power, weight and size. To address this, we propose a light-weight, evolving, and memory efficient cooperative approach for estimating the map of an environment with a team of UAVs. Additionally, we present proofof-concept experiments with real-life flights, showing that we can estimate maps using an off-the-shelf web-camera.

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Section: Related Workmentioning

confidence: 99%

Section: A Automatic Landmark Detectionmentioning

confidence: 99%

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Towards Evolving Cooperative Mapping for Large-Scale UAV Teams

Yourdshahi

Angelov

Marcolino

et al. 2018

2018 IEEE Symposium Series on Computational Intelligence (SSCI)

Self Cite

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“…Although image-based localization has been studied for a long time in the fields of robotics [4], [5] and augmented reality [6], [7], it has only been pursued over the past decade for mobile phones in indoor scenarios [8]. The proposed methods can be categorized into two classes [9], image retrieval-based (fingerprinting-based) [10], [11] and landmark-based (e.g.…”

Section: Introductionmentioning

confidence: 99%

OCRAPOSE II: An OCR-based indoor positioning system using mobile phone images

Sadeghi¹,

Valaee²,

Shirani³

2017

Preprint

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In this paper, we propose an OCR (optical character recognition)-based localization system called OCRAPOSE II, which is applicable in a number of indoor scenarios including office buildings, parkings, airports, grocery stores, etc. In these scenarios, characters (i.e. texts or numbers) can be used as suitable distinctive landmarks for localization. The proposed system takes advantage of OCR to read these characters in the query still images and provides a rough location estimate using a floor plan. Then, it finds depth and angle-of-view of the query using the information provided by the OCR engine in order to refine the location estimate. We derive novel formulas for the query angle-of-view and depth estimation using image line segments and the OCR box information. We demonstrate the applicability and effectiveness of the proposed system through experiments in indoor scenarios. It is shown that our system demonstrates better performance compared to the state-of-the-art benchmarks in terms of location recognition rate and average localization error specially under sparse database condition.

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“…In this case, some kind of guided or automated pretraining is needed in order to learn visual patterns in the environment, which are recognized and used later as visual clues at the navigation stage. Examples of guided pretraining methods are [1] and [2]. In [3, 4], during the navigation, the position is determined matching the online image with previously recorded images.…”

Section: Introductionmentioning

confidence: 99%

A Visual-Aided Inertial Navigation and Mapping System

Munguía

Nuño

Aldana

et al. 2016

International Journal of Advanced Robotic Systems

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State estimation is a fundamental necessity for any application involving autonomous robots. This paper describes a visual-aided inertial navigation and mapping system for application to autonomous robots. The system, which relies on Kalman filtering, is designed to fuse the measurements obtained from a monocular camera, an inertial measurement unit (IMU) and a position sensor (GPS). The estimated state consists of the full state of the vehicle: the position, orientation, their first derivatives and the parameter errors of the inertial sensors (i.e., the bias of gyroscopes and accelerometers). The system also provides the spatial locations of the visual features observed by the camera. The proposed scheme was designed by considering the limited resources commonly available in small mobile robots, while it is intended to be applied to cluttered environments in order to perform fully vision-based navigation in periods where the position sensor is not available. Moreover, the estimated map of visual features would be suitable for multiple tasks: i) terrain analysis; ii) three-dimensional (3D) scene reconstruction; iii) localization, detection or perception of obstacles and generating trajectories to navigate around these obstacles; and iv) autonomous exploration. In this work, simulations and experiments with real data are presented in order to validate and demonstrate the performance of the proposal.

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Autonomous visual self-localization in completely unknown environment

Cited by 4 publications

References 21 publications

Towards Evolving Cooperative Mapping for Large-Scale UAV Teams

Towards Evolving Cooperative Mapping for Large-Scale UAV Teams

OCRAPOSE II: An OCR-based indoor positioning system using mobile phone images

A Visual-Aided Inertial Navigation and Mapping System

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