Visual Navigation for Mobile Robots: A Survey

Bonin‐Font, Francisco; Ortiz, Alberto; Oliver, Gabriel

doi:10.1007/s10846-008-9235-4

Cited by 629 publications

(364 citation statements)

References 97 publications

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“…They are exceptionally critical when operating in urban environment and during inspection tasks. In such applications robot is required not only to avoid obstacles (even when flying on high altitude) but also to position itself precisely to execute given tasks [10], [19], [20] The scenario presented in section V require utilization of precise navigation as well. To support UAV with necessary capabilities the following methods were provided: 1) Calculating position of Points Of Interest (later referred to as POI) in the area photographed from high altitude.…”

Section: Navigation Methodsmentioning

confidence: 99%

Visual simulator for MavLink-protocol-based UAV, applied for search and analyze task

Śmigielski

Raczynski

Gosek

2017

Annals of Computer Science and Information Systems

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Abstract-In this paper the authors present the results of research to develop the visual system for autonomous flying agent. The core elements of the vision system which were designed and implemented in the earlier stage of the project are brought together. The second aim is to show capabilities of a simulation environment designed and developed by the authors in order to enable testing of the vision systems (dedicated for Unmanned Aerial Vehicles) in the artificial environment. The first section of the paper introduces the testing (simulation) environment for MavLink-protocol-based autonomous flying robots. Next, the core elements of a vision system, designed for Unmanned Aerial Vehicle (UAV), are discussed. This includes pre-processing and vectorization algorithms, object recognition methods and fast three-dimensional model construction. The third part introduces a set of algorithms for robot navigation, solely based on vision and altitude sensor and compass. The paper concludes with the description of the tests and presentation of results where designed simulator was applied to show mentioned vision system elements operating together to execute complex task.

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Section: Navigation Methodsmentioning

confidence: 99%

Visual simulator for MavLink-protocol-based UAV, applied for search and analyze task

Śmigielski

Raczynski

Gosek

2017

Annals of Computer Science and Information Systems

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“…The topics introduced are: navigation [20], safety and correctness (intersections navigation) [21], advanced safety and formal methods [22], multi-vehicle coordination [23], simultaneous localization and mapping (SLAM) [24], estimation [25], distributed systems [26], and visual inertial navigation [27]. Phase III -Public Demonstrations In this phase groups showcase their work during a public demonstration event.…”

Section: Teaching and Learning Activitiesmentioning

confidence: 99%

Duckietown: An Innovative Way to Teach Autonomy

Tani

Paull

Zuber

et al. 2017

Educational Robotics in the Makers Era

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Abstract. Teaching robotics is challenging because it is a multidisciplinary, rapidly evolving and experimental discipline that integrates cutting-edge hardware and software. This paper describes the course design and first implementation of Duckietown, a vehicle autonomy class that experiments with teaching innovations in addition to leveraging modern educational theory for improving student learning. We provide a robot to every student, thanks to a minimalist platform design, to maximize active learning; and introduce a role-play aspect to increase team spirit, by modeling the entire class as a fictional start-up (Duckietown Engineering Co.). The course formulation leverages backward design by formalizing intended learning outcomes (ILOs) enabling students to appreciate the challenges of: (a) heterogeneous disciplines converging in the design of a minimal self-driving car, (b) integrating subsystems to create complex system behaviors, and (c) allocating constrained computational resources. Students learn how to assemble, program, test and operate a self-driving car (Duckiebot) in a model urban environment (Duckietown), as well as how to implement and document new features in the system. Traditional course assessment tools are complemented by a full scale demonstration to the general public. The "duckie" theme was chosen to give a gender-neutral, friendly identity to the robots so as to improve student involvement and outreach possibilities. All of the teaching materials and code is released online in the hope that other institutions will adopt the platform and continue to evolve and improve it, so to keep pace with the fast evolution of the field.

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“…The abundant amount of information provided by images, coupled with the technological improvements in computing, has spawned the development of a large number of different visual-system approaches [7]. Of particular relevance to the work presented here, are the monocular mapless-based visual navigation systems in the sub-categories of optical-flow, ground-plane detection, and appearance-based approaches.…”

Section: Related Workmentioning

confidence: 99%

“…The most commonly used monocular approaches to obstacle avoidance involve the use of appearance-based features [3] [4] [5], or motion information [1] [6]. However, the sole use of either approach contains inherent assumptions and/or is highly sensitive to camera motion [2] [7]. Our approach takes advantage of both information sources, towards the goal of a more robust and adaptive system.…”

Section: Introductionmentioning

confidence: 99%

Ground-plane classification for robot navigation: Combining multiple cues toward a visual-based learning system

Löw

Manzanera

2010

2010 11th International Conference on Control Automation Robotics &Amp; Vision

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Abstract-This paper describes a vision-based ground-plane classification system for autonomous indoor mobile-robot that takes advantage of the synergy in combining together multiple visual-cues. A priori knowledge of the environment is important in many biological systems, in parallel with their reactive systems. As such, a learning model approach is taken here for the classification of the ground/object space, initialised through a new Distributed-Fusion (D-Fusion) method that captures colour and textural data using Superpixels. A Markov Random Field (MRF) network is then used to classify, regularise, employ a priori constraints, and merge additional ground/object information provided by other visual cues (such as motion) to improve classification images. The developed system can classify indoor test-set ground-plane surfaces with an average true-positive to false-positive rate of 90.92% to 7.78% respectively on test-set data. The system has been designed in mind to fuse a variety of different visual-cues. Consequently it can be customised to fit different situations and/or sensory architectures accordingly.

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Visual Navigation for Mobile Robots: A Survey

Cited by 629 publications

References 97 publications

Visual simulator for MavLink-protocol-based UAV, applied for search and analyze task

Visual simulator for MavLink-protocol-based UAV, applied for search and analyze task

Duckietown: An Innovative Way to Teach Autonomy

Ground-plane classification for robot navigation: Combining multiple cues toward a visual-based learning system

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